{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dislocation_SDVPN calculation style\n",
    "\n",
    "**Lucas M. Hale**, [lucas.hale@nist.gov](mailto:lucas.hale@nist.gov?Subject=ipr-demo), *Materials Science and Engineering Division, NIST*.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The dislocation_SDVPN calculation style predicts a dislocation's planar spreading using the semidiscrete variational Peierls-Nabarro method.  The solution finds the disregistry (difference in displacement above and below the slip plane) that minimizes the dislocation's energy.  The energy term consists of two primary components: an elastic component due to the dislocation interacting with itself, and a misfit component arising from the formation of a generalized stacking fault along the dislocation's spreading.\n",
    "\n",
    "### Version notes\n",
    "\n",
    "- 2018-09-25: Notebook added\n",
    "- 2019-07-30: Notebook setup and parameters changed.\n",
    "- 2020-09-22: Notebook updated to reflect changes in the calculation method due to updates in atomman's Volterra class solution generators.  Setup and parameter definitiions streamlined.\n",
    "\n",
    "### Additional dependencies\n",
    "\n",
    "### Disclaimers\n",
    "\n",
    "- [NIST disclaimers](http://www.nist.gov/public_affairs/disclaimer.cfm)\n",
    "- The calculation method solves the problem using a 2D generalized stacking fault energy map.  Better results may be possible by measuring a full 3D map, but that would require adding a new calculation for the 3D variation.\n",
    "- The implemented method is suited for dislocations with planar spreading. It is not suited for dislocations that spread on multiple atomic planes, like the a/2<111> bcc screw dislocation.\n",
    "- While the solution is taken at discrete points that (typically) correspond to atomic sites, the underlying method is still a continuum solution that does not fully account for the atomic nature of the dislocation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method and Theory\n",
    "\n",
    "This calculation method is a wrapper around the atomman.defect.SDVPN class.  More details on the method and theory can be found in the [associated tutorial within the atomman documentation](https://www.ctcms.nist.gov/potentials/atomman/tutorial/04.4._Semidiscrete_Variational_Peierls-Nabarro.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1. Library imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries needed by the calculation. The external libraries used are:\n",
    "\n",
    "- [numpy](http://www.numpy.org/)\n",
    "\n",
    "- [scipy](https://scipy.org/scipylib/)\n",
    "\n",
    "- [matplotlib](https://matplotlib.org/)\n",
    "\n",
    "- [atomman](https://github.com/usnistgov/atomman)\n",
    "\n",
    "- [iprPy](https://github.com/usnistgov/iprPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook last executed on 2021-03-08 using iprPy version 0.10.4\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "from pathlib import Path\n",
    "import os\n",
    "import datetime\n",
    "from copy import deepcopy\n",
    "\n",
    "# http://www.numpy.org/\n",
    "import numpy as np \n",
    "\n",
    "# https://matplotlib.org/\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# https://github.com/usnistgov/atomman \n",
    "import atomman as am\n",
    "import atomman.lammps as lmp\n",
    "import atomman.unitconvert as uc\n",
    "\n",
    "# https://github.com/usnistgov/iprPy\n",
    "import iprPy\n",
    "\n",
    "print('Notebook last executed on', datetime.date.today(), 'using iprPy version', iprPy.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2. Default calculation setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify calculation style\n",
    "calc_style = 'dislocation_SDVPN'\n",
    "\n",
    "# If workingdir is already set, then do nothing (already in correct folder)\n",
    "try:\n",
    "    workingdir = workingdir\n",
    "\n",
    "# Change to workingdir if not already there\n",
    "except:\n",
    "    workingdir = Path('calculationfiles', calc_style)\n",
    "    if not workingdir.is_dir():\n",
    "        workingdir.mkdir(parents=True)\n",
    "    os.chdir(workingdir)\n",
    "    \n",
    "# Initialize connection to library\n",
    "library = iprPy.Library(load=['lammps_potentials'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Assign values for the calculation's run parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1. Load initial unit cell system\n",
    "\n",
    "- __ucell__ is an atomman.System representing a fundamental unit cell of the system (required).  Here, this is loaded from the database for the prototype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avect =  [ 3.520,  0.000,  0.000]\n",
      "bvect =  [ 0.000,  3.520,  0.000]\n",
      "cvect =  [ 0.000,  0.000,  3.520]\n",
      "origin = [ 0.000,  0.000,  0.000]\n",
      "natoms = 4\n",
      "natypes = 1\n",
      "symbols = ('Ni',)\n",
      "pbc = [ True  True  True]\n",
      "per-atom properties = ['atype', 'pos']\n",
      "     id |   atype |  pos[0] |  pos[1] |  pos[2]\n",
      "      0 |       1 |   0.000 |   0.000 |   0.000\n",
      "      1 |       1 |   0.000 |   1.760 |   1.760\n",
      "      2 |       1 |   1.760 |   0.000 |   1.760\n",
      "      3 |       1 |   1.760 |   1.760 |   0.000\n"
     ]
    }
   ],
   "source": [
    "# Create ucell by loading prototype record\n",
    "ucell = am.load('crystal', potential_LAMMPS_id='1999--Mishin-Y--Ni--LAMMPS--ipr1', family='A1--Cu--fcc', database=library)\n",
    "\n",
    "print(ucell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Specify material elastic constants\n",
    "\n",
    "Simple input parameters:\n",
    "\n",
    "- __C_dict__ is a dictionary containing the unique elastic constants for the potential and crystal structure defined above. \n",
    "\n",
    "Derived parameters\n",
    "\n",
    "- __C__ is an atomman.ElasticConstants object built from C_dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_dict = {}\n",
    "C_dict['C11'] = uc.set_in_units(247.86, 'GPa')\n",
    "C_dict['C12'] = uc.set_in_units(147.83, 'GPa')\n",
    "C_dict['C44'] = uc.set_in_units(124.84, 'GPa')\n",
    "\n",
    "# -------------- Derived parameters -------------- #\n",
    "# Build ElasticConstants object from C_dict terms\n",
    "C = am.ElasticConstants(**C_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Specify the defect parameters\n",
    "\n",
    "- __gammasurface_file__ gives the path to a data model file containing 2D gamma surface results.  This can be a calc_stacking_fault_map_2D record.\n",
    "\n",
    "- __burgers__ is the crystallographic Miller Burgers vector for the dislocation. \n",
    "\n",
    "- __ξ_uvw__ is the Miller \\[uvw\\] line vector direction for the dislocation.  The angle between burgers and ξ_uvw determines the dislocation's character\n",
    "\n",
    "- __slip_hkl__ is the Miller (hkl) slip plane for the dislocation.\n",
    "    \n",
    "- __m__ is the Cartesian vector of the final system that the dislocation solution's m vector (in-plane, perpendicular to ξ) should align with.  Limited to being parallel to one of the three Cartesian axes.  \n",
    "\n",
    "- __n__ is the Cartesian vector of the final system that the dislocation solution's n vector (slip plane normal) should align with.  Limited to being parallel to one of the three Cartesian axes. \n",
    "\n",
    "- __gamma__ is a GammaSurface object.  Here, it is loaded from gammasurface_file.  Note that gamma and volterra must be for the same plane.\n",
    "\n",
    "- __volterra__ is a VolterraDislocation object for the dislocation type of interest.  Here, it is created based on the elastic constants, unit cell and dislocation parameters above.  Note that gamma and volterra must be for the same plane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gammasurface_file = '../stacking_fault_map_2D/gamma.json'\n",
    "gamma = am.defect.GammaSurface(model=gammasurface_file)\n",
    "\n",
    "# fcc a/2 <110>{111} dislocations\n",
    "burgers = 0.5 * np.array([ 1., -1., 0.])\n",
    "slip_hkl = [1, 1, 1]\n",
    "\n",
    "# Line direction determines dislocation character\n",
    "ξ_uvw = [ 1, 1,-2] # 90 degree edge\n",
    "#ξ_uvw = [ 1, 0,-1] # 60 degree mixed\n",
    "#ξ_uvw = [ 1,-2, 1] # 30 degree mixed\n",
    "#ξ_uvw = [ 1,-1, 0] # 0 degree screw\n",
    "\n",
    "# Best choice for m + n as it works for non-cubic systems\n",
    "m = [0,1,0]\n",
    "n = [0,0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Specify calculation-specific run parameters\n",
    "\n",
    "- __xmax__: The maximum value of the x-coordinates to use for the points where the disregistry is evaluated.  The solution is centered around x=0, therefore this also corresponds to the minimum value of x used.  The set of x-coordinates used is fully defined by giving at least two of xmax, xstep and xnum.\n",
    "\n",
    "- __xstep__: The step size (delta x) value between the x-coordinates used to evaluate the disregistry.  The set of x-coordinates used is fully defined by giving at least two of xmax, xstep and xnum.\n",
    "\n",
    "- __xnum__: The total number of x-coordinates at which to evaluate the disregistry.  The set of x-coordinates used is fully defined by giving at least two of xmax, xstep and xnum.\n",
    "\n",
    "- __min_method__: The scipy.optimize.minimize method style to use when solving for the disregistry.  Default value is 'Powell', which seems to do decently well for this problem.\n",
    "\n",
    "- __min_options__: Allows for the specification of the options dictionary used by scipy.optimize.minimize. This is given as \"key value key value...\".\n",
    "\n",
    "- __min_cycles__: Specifies the number of times to run the minimization in succession.  The minimization algorithms used by the underlying scipy code often benefit from restarting and rerunning the minimized configuration to achive a better fit.  Default value is 10.\n",
    "\n",
    "- __cutofflongrange__: The radial cutoff (in distance units) to use for the long-range elastic energy.  The long-range elastic energy is configuration-independent, so this value changes the dislocation's energy but not the computed disregistry profile. Default value is 1000 Angstroms.\n",
    "\n",
    "- __tau_xy__: Shear stress (in units of pressure) to apply to the system. Default value is 0 GPa.\n",
    "\n",
    "- __tau_yy__: Normal stress (in units of pressure) to apply to the system. Default value is 0 GPa.\n",
    "\n",
    "- __tau_yz__: Shear stress (in units of pressure) to apply to the system. Default value is 0 GPa.\n",
    "\n",
    "- __alpha__: Coefficient(s) (in pressure/length units) of the non-local energy correction term to use.  Default value is 0.0, meaning this correction is not applied.\n",
    "\n",
    "- __beta_xx, beta_yy, beta_zz, beta_xy, beta_xz, beta_yz__: Components of the surface energy coefficient tensor (in units pressure-length) to use. Default value is 0.0 GPa-Angstrom for all, meaning this correction is not applied.\n",
    "\n",
    "- __cdiffelastic, cdiffsurface, cdiffstress__: Booleans indicating how the dislocation density (derivative of disregistry) is computed within the elastic, surface and stress terms, respectively. If True, central difference is used, otherwise only the change between the current and previous points is used. Default values are True for cdiffsurface, and False for the other two.\n",
    "\n",
    "- __halfwidth__: The arctan disregistry halfwidth (in length units) to use for creating the initial disregistry guess.\n",
    "\n",
    "- __normalizedisreg__: Boolean indicating how the disregistry profile is handled.  If True (default), the disregistry is scaled such that the minimum x value has a disregistry of 0 and the maximum x value has a disregistry equal to the dislocation's Burgers vector.  Note that the disregistry for these endpoints is fixed, so if you use False the initial disregistry should be close to the final solution.\n",
    "\n",
    "- __fullstress__: Boolean indicating which of two stress formulas to use.  True uses the original full formulation, while False uses a newer, simpler representation.  Default value is True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmax = None\n",
    "xstep = 1/5\n",
    "xnum = 200\n",
    "xscale = True\n",
    "\n",
    "min_method = 'Powell'\n",
    "min_options = {}\n",
    "min_options['disp'] = True # will display convergence info\n",
    "min_options['xtol'] = 1e-6 # smaller convergence tolerance than default\n",
    "min_options['ftol'] = 1e-6 # smaller convergence tolerance than default\n",
    "#min_options['maxiter'] = 2 # for testing purposes\n",
    "min_cycles = 10\n",
    "\n",
    "cutofflongrange = uc.set_in_units(1000, 'angstrom')\n",
    "halfwidth = uc.set_in_units(5, 'angstrom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Define calculation function(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1. peierlsnabarro()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def peierlsnabarro(ucell, C, burgers, ξ_uvw, slip_hkl, gamma,\n",
    "                   m=[0,1,0], n=[0,0,1],\n",
    "                   cutofflongrange=uc.set_in_units(1000, 'angstrom'),\n",
    "                   tau=np.zeros((3,3)), alpha=[0.0], beta=np.zeros((3,3)),\n",
    "                   cdiffelastic=False, cdiffsurface=True, cdiffstress=False,\n",
    "                   fullstress=True,\n",
    "                   halfwidth=uc.set_in_units(1, 'angstrom'),\n",
    "                   normalizedisreg=True,\n",
    "                   xnum=None, xmax=None, xstep=None, xscale=False,\n",
    "                   min_method='Powell', min_options={}, min_cycles=10):\n",
    "    \"\"\"\n",
    "    Solves a Peierls-Nabarro dislocation model.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ucell : atomman.System\n",
    "        The unit cell to use as the seed for the dislocation system.  Note that\n",
    "        only box information is used and not atomic positions.\n",
    "    C : atomman.ElasticConstants\n",
    "        The elastic constants associated with the bulk crystal structure\n",
    "        for ucell.\n",
    "    burgers : array-like object\n",
    "        The dislocation's Burgers vector given as a Miller or\n",
    "        Miller-Bravais vector relative to ucell.\n",
    "    ξ_uvw : array-like object\n",
    "        The dislocation's line direction given as a Miller or\n",
    "        Miller-Bravais vector relative to ucell.\n",
    "    slip_hkl : array-like object\n",
    "        The dislocation's slip plane given as a Miller or Miller-Bravais\n",
    "        plane relative to ucell.\n",
    "    m : array-like object, optional\n",
    "        The m unit vector for the dislocation solution.  m, n, and ξ\n",
    "        (dislocation line) should be right-hand orthogonal.  Default value\n",
    "        is [0,1,0] (y-axis).\n",
    "    n : array-like object, optional\n",
    "        The n unit vector for the dislocation solution.  m, n, and ξ\n",
    "        (dislocation line) should be right-hand orthogonal.  Default value\n",
    "        is [0,0,1] (z-axis). n is normal to the dislocation slip plane.\n",
    "    cutofflongrange : float, optional\n",
    "        The cutoff distance to use for computing the long-range energy.\n",
    "        Default value is 1000 angstroms.\n",
    "    tau : numpy.ndarray, optional\n",
    "        A (3,3) array giving the stress tensor to apply to the system\n",
    "        using the stress energy term.  Only the xy, yy, and yz components\n",
    "        are used.  Default value is all zeros.\n",
    "    alpha : list of float, optional\n",
    "        The alpha coefficient(s) used by the nonlocal energy term.  Default\n",
    "        value is [0.0].\n",
    "    beta : numpy.ndarray, optional\n",
    "        The (3,3) array of beta coefficient(s) used by the surface energy\n",
    "        term.  Default value is all zeros.\n",
    "    cdiffelastic : bool, optional\n",
    "        Flag indicating if the dislocation density for the elastic energy\n",
    "        component is computed with central difference (True) or simply\n",
    "        neighboring values (False).  Default value is False.\n",
    "    cdiffsurface : bool, optional\n",
    "        Flag indicating if the dislocation density for the surface energy\n",
    "        component is computed with central difference (True) or simply\n",
    "        neighboring values (False).  Default value is True.\n",
    "    cdiffstress : bool, optional\n",
    "        Flag indicating if the dislocation density for the stress energy\n",
    "        component is computed with central difference (True) or simply\n",
    "        neighboring values (False).  Only matters if fullstress is True.\n",
    "        Default value is False.\n",
    "    fullstress : bool, optional\n",
    "        Flag indicating which stress energy algorithm to use.  Default\n",
    "        value is True.\n",
    "    halfwidth : float, optional\n",
    "        A dislocation halfwidth guess to use for generating the initial\n",
    "        disregistry guess.  Does not have to be accurate, but the better the\n",
    "        guess the fewer minimization steps will likely be needed.  Default\n",
    "        value is 1 Angstrom.\n",
    "    normalizedisreg : bool, optional\n",
    "        If True, the initial disregistry guess will be scaled such that it\n",
    "        will have a value of 0 at the minimum x and a value of burgers at the\n",
    "        maximum x.  Default value is True.  Note: the disregistry of end points\n",
    "        are fixed, thus True is usually preferential.\n",
    "    xnum :  int, optional\n",
    "        The number of x value points to use for the solution.  Two of xnum,\n",
    "        xmax, and xstep must be given.\n",
    "    xmax : float, optional\n",
    "        The maximum value of x to use.  Note that the minimum x value will be\n",
    "        -xmax, thus the range of x will be twice xmax.  Two of xnum, xmax, and\n",
    "        xstep must be given.\n",
    "    xstep : float, optional\n",
    "        The delta x value to use, i.e. the step size between the x values used.\n",
    "        Two of xnum, xmax, and xstep must be given.\n",
    "    xscale : bool, optional\n",
    "        Flag indicating if xmax and/or xstep values are to be taken as absolute\n",
    "        or relative to ucell's a lattice parameter.  Default value is False,\n",
    "        i.e. the x parameters are absolute and not scaled.\n",
    "    min_method : str, optional\n",
    "        The scipy.optimize.minimize method to use.  Default value is\n",
    "        'Powell'.\n",
    "    min_options : dict, optional\n",
    "        Any options to pass on to scipy.optimize.minimize. Default value\n",
    "        is {}.\n",
    "    min_cycles : int, optional\n",
    "        The number of minimization runs to perform on the system.  Restarting\n",
    "        after obtaining a solution can help further refine to the best pathway.\n",
    "        Default value is 10. \n",
    "    \"\"\"\n",
    "\n",
    "    # Solve Volterra dislocation\n",
    "    volterra = am.defect.solve_volterra_dislocation(C, burgers, ξ_uvw=ξ_uvw,\n",
    "                                                    slip_hkl=slip_hkl, box=ucell.box,\n",
    "                                                    m=m, n=n)\n",
    "    \n",
    "    # Generate SDVPN object\n",
    "    pnsolution = am.defect.SDVPN(volterra=volterra, gamma=gamma,\n",
    "                                 tau=tau, alpha=alpha, beta=beta,\n",
    "                                 cutofflongrange=cutofflongrange,\n",
    "                                 fullstress=fullstress, cdiffelastic=cdiffelastic,\n",
    "                                 cdiffsurface=cdiffsurface, cdiffstress=cdiffstress,\n",
    "                                 min_method=min_method, min_options=min_options)\n",
    "\n",
    "    # Scale xmax and xstep by alat\n",
    "    if xscale is True:\n",
    "        if xmax is not None:\n",
    "            xmax *= ucell.box.a\n",
    "        if xstep is not None:\n",
    "            xstep *= ucell.box.a\n",
    "    \n",
    "    # Generate initial disregistry guess\n",
    "    x, idisreg = am.defect.pn_arctan_disregistry(xmax=xmax, xstep=xstep, xnum=xnum,\n",
    "                                                 burgers=pnsolution.burgers,\n",
    "                                                 halfwidth=halfwidth,\n",
    "                                                 normalize=normalizedisreg)\n",
    "    \n",
    "    # Set up loop parameters\n",
    "    cycle = 0\n",
    "    disregistries = [idisreg]\n",
    "    minimization_energies = [pnsolution.total_energy(x, idisreg)]\n",
    "    \n",
    "    # Run minimization for min_cycles\n",
    "    pnsolution.x = x\n",
    "    pnsolution.disregistry = idisreg\n",
    "    while cycle < min_cycles:\n",
    "        cycle += 1\n",
    "        pnsolution.solve()\n",
    "        disregistries.append(pnsolution.disregistry)\n",
    "        minimization_energies.append(pnsolution.total_energy())\n",
    "\n",
    "    # Initialize results dict\n",
    "    results_dict = {}\n",
    "    results_dict['SDVPN_solution'] = pnsolution\n",
    "    results_dict['minimization_energies'] = minimization_energies\n",
    "    results_dict['disregistry_profiles'] = disregistries\n",
    "    \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Run calculation function(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905269\n",
      "         Iterations: 25\n",
      "         Function evaluations: 169106\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 2\n",
      "         Function evaluations: 16780\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8565\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8620\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8937\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 9090\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8976\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 9085\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8846\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 4.905263\n",
      "         Iterations: 1\n",
      "         Function evaluations: 8856\n"
     ]
    }
   ],
   "source": [
    "results_dict = peierlsnabarro(ucell, C, burgers, ξ_uvw, slip_hkl, gamma,\n",
    "                              m = m,\n",
    "                              n = n,\n",
    "                              cutofflongrange = cutofflongrange,\n",
    "                              #tau = tau,    \n",
    "                              #alpha = alpha,\n",
    "                              #beta = beta,\n",
    "                              #cdiffelastic = cdiffelastic,\n",
    "                              #cdiffsurface = cdiffsurface,\n",
    "                              #cdiffstress = cdiffstress,\n",
    "                              #fullstress = fullstress,\n",
    "                              halfwidth = halfwidth,\n",
    "                              #normalizedisreg = normalizedisreg,\n",
    "                              xnum = xnum,\n",
    "                              xstep = xstep,\n",
    "                              xmax = xmax,\n",
    "                              xscale = xscale,\n",
    "                              min_method = min_method,\n",
    "                              min_options = min_options,\n",
    "                              min_cycles = min_cycles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['SDVPN_solution', 'minimization_energies', 'disregistry_profiles'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Report results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Define units for outputting values\n",
    "\n",
    "- __length_unit__ is the unit of length to display results in.\n",
    "- __energy_per_length_unit__ is the unit of energy per length to display dislocation energies in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "length_unit = 'Å'\n",
    "energyperlength_unit = 'eV/Å'\n",
    "energyperarea_unit = 'eV/Å^2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 Solution\n",
    "\n",
    "SDVPN_solution in the results dictionary is an am.Defect.SDVPN object which contains the final disregistry information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_dict['SDVPN_solution'].disregistry_plot(length_unit=length_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vX9dImZfLbHXmmdcVUuH2VaV5U2MpaedcJanhCtkQ60a8qEfGfVmJ2UymqFNjXq9ql3Vq3HeMixRVilT78pUSKsLjUW7iIamGizSJiYdAm0Nh4iLcbEZ/As+SRFZjC/EozeYy+tq0LVmELqvrzNMQPCSd1sb7ThAeUlbmWbqitNND9LGyTfxrb3tp6+06qhIAzSlmq7t0ZM01gArAD4nILUqpswDerpR6g4j8jXbM1QDuN//3cAAvAfBwpdTdAPwkgKswe+zfrpR6jYjcfrB/QlTU/unxl36X+QvNWD+y+KCjct3yThhSpWZjPeL0EEfl2YODuKrWoZ0YdflwEEZAzGPNsdd4G8ea97WVFuJAPHw4CFetjYtlo83mOAAPcRlrn1zG2qcQY72Xchlrn1zG2qeCqtKMizjXUpWab4dLQ/AQzsN2pYWweMNjCB7CFe29xEPcaz14iEuMg1THr2FMl2Ll2taRZa5F5F8WVWgROQ/gfQDuRYc9EcArZaa/AHBXpdRnAXgcgDeIyKfnhvoNAB5/gDc/KupQ9R3FLZ1vd7epbedaX9XaKTbWXLV26PA6MdJlDejEGLLWMtZ5QGoHGesQlpqNdQhbzcbaV7V2XY+vat13DtjbqrUuNtatVesOsbEu6pFz3jjWg4O4zLOFkrDxDuCyGQ9BMQAPCUA8fF0dXToyeEgREJcy13GrWgPzDo1zNKTt32nVkTXXupRS5wB8CYC30dS9APyTNv7I/Hddv4+KOhG6+orvMX9BUU73kIut6wTAy0dfbP4yoGptiavWARoSvRcVFbX3Cqpak0Kq1tbaQ+rMGFK1ZgVVrVktVevjaKyjunXkP92UUmcA/B6A/ywid/J0y5Iu1LT1FaiUugbANQBw5ZVXDrilUVEHKzWvCktZmRsI6xoNFNKWp7wAuGnjft31BDbazFrrWAfjIVzh5qq1NpaMKoIB0XuhnRh1JMSHg7gq0VaFm9NDxoSA5Pqx5oc4b2LkBjFJpnVEpLmRh63Wx1xpHlNl2cVW81oLD0mIrdbmM88mRkY+Mm1DZELBXly1Tj3zIeJKdK3VmxpRGGP1NzZUiyqbxJgvJV3eJ1WTGp0ZS0mM+4vTRYpmxWIXTerlsDmaL5vz09N6ZMTyFXWKNCf2umNcVakZ01cmUBqX3ZSp0blR7+TIsXxJmRivh6QA6o3V35tMV1F93MVxxlLDXEuxfovjmbtOCzFe7y722tu10cFee7s2lpX5/qez13UN5MeirtlbIY2XTouO9COslMowM9a/JSK/33LIRwDcRxvfG8A/O35vSUSuFZGrROSqyy+/fG9ueFTUPsqqWreolasOlctY++Qw1j4d5CZGc605DkoPcRhrn1zG2qdBUXse8+ySy1h7r9dhrH06KGNtHdtirI2xAw8prWYytrHuko/DdqWH8NoQPKQumcM2j+VmM7oS5rAZ8ZiuH9sXkjxynPCQ1370l/pf/hFTxELadWTN9TwJ5DoA7xORF3Qc9hoA/5ea6REA7hCRfwFwA4DHKqUuVUpdCuCx899FRR1rXX2v/+Q+YI6HnEf7Dr2u3wMYhoeEcNmkkOg9e+36b2GuTYze613/JgdF77G4ah0irlqHKMR4s0KMN+ugNjGyYiXucBTCZVtrjyMecgIkUKiRdP47rTrKWMiXA/hWAO9WSr1j/rsfA3AlAIjISwFcj1kM3wcwi+L7jvncp5VSPw3gr+brniUinz7A2x4VtX/ScYyqWlaUpa6BxPeSFm8MHwAr81rxGv02cEWbcZGckY/VfJNzekj/hjGhnRj1eS8O4pj3Re1ZVWwtIURo06JdtSacQjst76tSb4zMD/lcG/Nmwg0y2i4EhKvUbNJ5Y+I40a+Xm8lQPCC600Q405qjBIdkXLP0HOva07WxbFLoT8saCTaxqk7q0Xxlk2BTWzttRsb9U0lq3J/TerS8v3VUZDZ24yKTeoSteanXjukbIdOee5NqhHGuIS1Vinw+LqsUIw0PqYoUiYGHmLiIlKtoPlUkaLTrSUplvJaSqSJcRC1fT1bUXgFaewzwkLK2cRB9XFXWe/RrP/RCHHfFL6O2jqy5FpE/hafDqIgIgO/rmHsZgJftw02LijoUWVXrqrtichbtJaCzCN855DTWPjmMtU8hnRhZIZ0YWS5j7V/rid5zyGWsfXIZa598bLVLLmPtk8tY+7RfxtonTuLgypwrPWTacFqIeey0duAhjJIQ8jFxra3MuQmNXXhIVdDeCEI+3HiI+ZpjHITTRHRZaSFW9F7nUquCfWh4COMgLe/XJ8FYL7CQKFOnt2YfFXWCJJQWcltypvW41hg+T9XaKV/V2iFf1TpEIebZXrv2UqtqHSKuWocohK1mcdU6REPQEjbiIeKq9UGJq9YhYi47RC4O23+967+OQmL6WCogpo81KC1kAEqyp5nX5cnJrY4ariNbuY6Kilrp6nM/aBrZqjYypZVukOsGb8uuxNdO/8awBgLgL7IrgZRaGyttzMY6JPOaM649DWPMuf44CGAiISE4yOyy9DnzdlhVa0dCSE1Vamaprc6M2ulyNtYpValHtKlRz6bOM9PgWlXrtHueq9IWHuJAQHjOtzFxrEptzp0WwpVoPemGj2UNboHe4Z95k2INhbHW+YQr3rPjFziIiY5MJUOmOUhX0kjRjDpRkcU84yBntMvWq9yTaoTNTJurR9jOC2NeTw/RGfeiSpGNNYSlTI0ujnWRQmlrdTJghocQ/qG9XlSh6PWzWtyWHuLCQ3SF4iF6fTEUD9GfdUlR2ekh+lm7srLeH1/7/ue2/xHHTspCqaJi5Toq6sjr6nM/aP6CM1JbTjc+vPyw5RkUgEdU/2T+TjneAnzG2pVx7evEGISH9O/EaK0N6MRoX685DtnE6DLWPrmMtU8uY+1d62GrXXIZa59cxjp0bYhCcBDf2rAujuuv5Yo24yCuro7cxIbxkKLsfk1WlBZSEy7ixEMI/1BWcxkHHkJV6YPCQ7iCHYKHWBscOT3khEkwS9Pp+ndaFSvXUVEnSfXsQ+Hy5kLrdNfvAYThICxXy3OPjkz0XpDxdletXfJVrV3yVa2daz1Va/dad9XaJV/V2rl2AEoyRG1V63U1lfVfGy4O2yc2zyHiduohGoSHhDc1XGov8ZCgtWukhZycqvVMkbm2Fc11VNQR1tX3fyaQay6wLJ14COYNJG5Lz+KK+rx1ebclZ6D6cNFcpeYqNletNSxFmMPmhjGOqnUIDsIKwUFYXhzEwWXzJkbhTYxjk9xV2ql3NtYZV63JTOttvjcy04lwpdlGPvQGMR4cxGGmXXOAbaZ1lIKr0tZaZV62jinwBkhWCJddO3AQwNyoaLdAN9eWkhp/h46PTJsMGbSGL4SLlJIaeMi0GS1TPqbNyIhenDYjI9awaEY4o4/rFS4yqUc4o+Egk3qEkYaOTBkPKTNs5qvHKdHORBTlaJkkAgBlmWKk4SIzPERLD9Hv16mJh6hSLZNFgHkzGf31o72PWOkhxxAPAaeHAHjde3+2/UYfU4lELKRN8R6JijouKnn3OVX1mtXb/Nvyz7FOrk8wwivOPDz8en3G2lW19hhr1ybG0E6Mrjlf9J5LbKzrkE2MY9PwqYAGMWysxwF4CBvrkCo1m+eQBBCXsfaudRhrn/Zyw2NINu+QCjev5TQRlxgPYeTDJa6GT0rz9Tt1VK1LLx7SvVZ50kNSV3rIMcRDEDc49pJS6vFKqb9TSn1AKfUjLfNjpdTvzuffppQ6N/99ppT6daXUu5VS71NK/ehB33aXormOijqiuvr+z+x/sGasH7nzd3js7t8aH/MNgNdv3B83bd7ff1khDWHIWFtVa4eG4CCsEByE5atau9d6qtYO+arWLvmq1i75qtYu+arWLvmq1u61e2ieQ6L32qrWPTUlSD8kxYONdojxZg7bFdNnXS8ZaxeHzbKM9bT/fcXGOiQ9hI11CB7CxjoED2Fj3QcPOWlV64UaqM5/LimlUgC/AuBqAA8A8GSl1APosKcCuF1EPh/ACwH8/Pz33wRgLCIPBvBQAP/3wngfBUUsJCrqiEo3qop3m1c1kGkfXtqmxm8//xfYAFUBATy8/Ce8pI9x1ivVlCyCpL9B4E2MQlVsl085rKq1Dwdh1lqXxV1zwxgy03rAS0ZV6pyMNrPVeoOVTTLadtXanB9pVd5Ncia+qrUTD/HhItqY56zMa553VKaTUOOt3e++DVe6uWaUpEaCsdY8ho24/jeUkhpV/LIZdeIggHnfcuMZHu/WOUaj1eM4MpJEMiNJhPGQ3TLDloaPJGr1HsONZooyJTxkZDab0e4bKVLzrE2RGK8PRekhtgfrj4e45MNDbAXgIbRSfxbOmsmcDns1y7leu077MAAfEJFbAUAp9SoATwTwN9oxTwTw3+c/vxrAL887eAuAbaXUCMAmgALAnevekL1WrFxHRR1BPf6BP9496UkLubyFtXb93hAjIMacOy3EVbVmY91s0DjrNu2hnRiNYwM7MRpzHmMdknHNxnpIg5iNUX/Ugo11SIOYIVVql7EOXXtYGddBCSB0bMharkqHrN2lJ7QrLcRaSzjIpOq/8bKkijY3m3GJ00I4TeSkpYcsdMNf/5T3mOOpGXPd9Q/AZUqpm7V/12iL7wVAj7D6yPx3aDtGRCoAdwC4O2ZG+yKAfwHwYQDPO0qduE/HV6uoqGMkNtYqJMqpabo3M6Zn3Wut6L3+373ZWId0YmRjfWidGENwECtqz1211sXG2le11sXG2le1Ni6XjLWvam3cRk8nRq5au9b6qtbGnMdYB1etNbmq1hZLTadZhnDZZQDiMQwPIfMcgIfYMX0BZ6zYaAekh7CxPo54SFszmZNrrFdRfA59UkSu6phre8PmbzldxzwMQA3gswFcCuAtSqkbF1Xww1Y011FRR0xGM4Kqgoz15gS1mRZS0270usYrLv1yPO1TN2JDVoZnokZ4xV2+zM1TcxSfzi3wOitNpPtihVjqJqATYwgOwtrTTYwOHMTSmD5sKXpP0UY9/W4ek9Fm85xSnrSOh2yN2GibYzsSb3VZzGxzTrXLTLNZ3qC1vDExV91r2SyntFY34vvZAp1vV61FYLCRKCU1/qZCRhYSov+s4x+lpMZ9yeki+uVwI5ppPUI2mizHu01mPMY6/jOpM5zR0JGdKsM2pYls5ys3qj/PdssMGxoOMm1JD9GbzeiPvpSJ+XqYpjYe0nEGKJ0q43UYgoekLcfuFx7CNQnXyb+Tqlr6vy+TPgLgPtr43gD+ueOYj8wRkLsA+DSApwB4nYiUAD6hlPozAFcBOBLm+hQ+DaKijq4e9yU/2f9ganm+GN905gvxort/Ne5MZp8ut6XbeNGlj8ZN21/QfVkuY80iY80bE8UR9cfGOqRqHdqJ0Zyj2xFQtfbhIK6MazbWugkB3JsYraq0ZZ4DNiLSsZsBKR4hVWqWy1j75DLW+ymuSocYh0LWRzyGbIDcpbUh+djWBsggPISq8tP+1+vDQ1zy4SHOtXuIhyRUpU5a8JDX/8V/63/jTp/+CsD9lFL3VUrlAJ4E4DV0zGsAfNv8528E8CYREcxQkK9SM20DeASAvz2g2+1VrFxHRR1RKWKp+3B9SzXtrYX7XTEdH5Aewsaaq9YuHZlOjAEVbstYj/s/RmysuWrtEhtrX9VaFxtrX9Val2/TIletdbGx9lWtdbGx3s+qtS421m1V6y75OGznWjLW3IgmzDyba3cCzDNz2dOg9BC6X6f93wuOIx7SZqxPgwRq7Q2NIlIppb4fwA2Y7RF9mYi8Vyn1LAA3i8hrAFwH4DeUUh/ArGL9pPnyXwHwcgDvwexD7uUi8q5hf83eKZrrqKgjoqCqNUurYj/ywvvwtNvfuMRCLq8v4Gm3vxEA2qvXAzozunKqfXJtYoyKilpPw7o6rt9NMqRqzQqpWltrB3Rm5Kp10PX2yLE+LVXrkIhKlohcD+B6+t1/036eYBa7x+sutP3+qCia66ioI6DHPuJZ0Fu3qbI22euyNtnrsgaS+bhuDNDv2z/zVoO3BoANqfDtd7wVN93lQYBo1RjFwCB9yDk2OVrRep6xjoT4cBAXax3aiVFHQkI7MepIiBcHoap1om1ctDcxUsU3796ouHmWX8AAACAASURBVElVabtK3R29x5sWN1M3S72hVbW5AYxvYyJXrfV53pjo68yoV6oZD2EN3tTYkWRSi6JOjCNA46FrJBZbvRjXkhj3RympMZ5IZq+ds9gc28cc9qTJrM6NZ+es9W5tzu3WObZGOludWZ0bF1F9O5UZy7dbZdjKzdg+/Xk6KbLl85i564q6NjZF2sleM3edFGbXRhd77eva6GKvfV0b00KM9yGdvU6njfEelpS1hbzd+CeOxKcTpIFRfCdW0VxHRR1x8e7ztt3oui6v2qM+L6/Pm8bap0HpIad7E2MSErXnMNbetZ5Ma5dcxtqnEGPtW+sy1j7tV1oIqySWms1ECB4yIcTDtZY57Amz1Y70EI7pC0kP2aUKthXbV/SvcDecHnLC8ZDTJoEasqHxxCp+3YiKOmR99Vc+2xj7zLNTTYPbRpe0Tvmj+AbgIQMQj5DoPWttgPG2r3ftpc5NjD6FdGJkcdU6RFy1DlFITjVrSE61r2q9X4pmYX2FZF6zTgoeclqq1lHdipXrqKgjoGaOfCQF4SBFZRhXVTVmhnS1QkJUVQNpildc9m/xtI+/zo7iu/u/BbqSPHgTI42FGsZg1B8PsRJC8tXaOuuPgwBAPVZY+K1qHNaJUT99HNqJUT9tHYKDACYSone8A1ramNOmRj0uzbdpcXtECIiOeFhpIeaxXLU2OzG6q9J21Xp1XTlvWiSjbc0bOAjF8nmq1IkVj9stX1vmWsOlajHxD654F5Iaf3OjdXLkuZJi+lrxkPl42mTY0h4njvGbNJkx3m3yZdyeL6bvYtWNi3BM38UyN86mTMuRgYtMymz5vJ4WI+P5XhWp8Xo4injIaCqotfeSdBqAh5QNmoBN2ydVIWeBTouiuY6KOkRx1dolVZG54PFcN13yQADANbe9CZfWO7g93cK1d38UbrrkAWvdRpex9sllrH1qM9b919LtCEoPWb8To8tY++Qy1j65jLVPLmPtXesw1j65jHWo9tRYO4xCm7E25h0buxgtCcFDLLSE8JBdR8QNx/RdrNy4iHFsaR7LaSGTsnstV7BD8BBf18ajjIe86Q0/0v8KToBE7CZLURELiYo6NH3VY55jjDnKSRX9jYqilug3nX0Afuqz/08AwAuvuNptrD1Va5d8VWuXfFXrEA1BPEKi9ywFRO+xuGodtHYA4sFV6xANwUOGxOcdVMY1a4hpYOMdImatg9YOSAsJiemzrrdY/3o58zpEQzKvR1PKuJ72/5KWlpHDnkmhcfw7rYqV66ioQ1RXJdfCQ8rawkOgjQ0PMH/P3603AABj1ABXn7vEOEjqybzWjucEEGHz7Kha98FBdOlISJMDOppbj81k7zqH0VC3Hq/GjTUnxrihKraBhPhwEJrXK9NsrDd5UyMhIHrCg4WDWIkg3QkhXKVm5GODKs/6vC+n2rVRkavUvk2MeiXaxkP2j8OuoZbJHDYOQs9JJBbyoSePJNrm4VJGSCk9ZFtNjXFX58ppkxFKMsKW5iinTYYz6QoBGamVQd6tM2xrx+7WOc6OVuOLlCaiN/25UObG2ZPdKjOep7tF1vkFcVqOjOd/NXXjIQLtvY3xkKlCnRMeon8H0J4O6dT8spwWNKb5SntMQ/GQNv3x9c9wzp9ECWLluk17Yq6VUimAcwAum//qkwA+JCKnM1U9Ksoju2rdXQWx0kIYB+FOjXNNktmnyGazfqXSEHdmDKhSs7HmqrVLLmPtXWux1L2Xuo21Ry5j7ZPLWHvXeqL3XHIZa598CSAuuYz1QaoOqLLZaSHmR6mrau1rJsNdHnVNaM6XJqKL00MuetJEjLWcHkJpIU48ZDoAD5muj4f4cBDnWq5o98BDTqOxXihG8dla+x5RSl2ilPoepdSNAO4A8PcA3jr/9/cA7lBKvXF+zF325uZGRZ18Der0pb3nP/TCBwEAP/ix6/GKD7wYj7zjve61vqq162o9VWvn2gHRe/ZGxN5LDy96z1O1dq71VK2daz1Va5d8VWv32gHG+wCr1rq4Ejfk9DYb7xANwUN2HWbZpwvl+qxUSBdH1l7iISFiPCQqaqiCXwVKqUsB/DiA7wawCeBvALwKwK0APoWZYb8UwOcBeDiAXwbwPKXUSwD8rIjcvjc3PSrqeOorn/BcQDOfadl0IhOqNNNBZmkhq2NVXQOpOQ8Aj7z9Pbjm47OujArAFdWdeNrHXgtJFW6664P8N5IyroXHjqo1/y1stGsHHuLDQeocUNLe2t2XFuIy3tYmxoCqdesmRp0eybvNJBtr3sTICSF6VXd7ZLoJrlJvUWlOX+vHQ7o3Ofqq1C4zzVVqviw2z670ENYQ4z3DQfTx6jnKmxh1dASw8ZESq4SQUkbG/eFLD9HTQiZNhi0NIJ5Ihi21ekw5TWRCY/2+3mly4/myW2c4q6WJXKzG2Or4otaaHtIXDwlMD7HwkDHhIY70EF1pj7QQfW0tfGwYHvKWP/gv7TfkFEig0MToSkvrfMW8FcBtAH4SwKtE5KOug5VS98asF/w1AJ6KmfGOijqV+sonPNcY86YYJx5COIiqu3GR7/jYm1u7NH7Hx2/qZ641+Yy1ONJDfMY6LD2ExiF4iCd6zyVf9J5Lvug9l9hYd5mf1rVkrLlhjEtsrEOaywyqUjuMdejaEDEOEsKP8rEl+qNSXNF2pYWwhjWX4fQQ88Wx4+i8xOkhQXjIkPSQfcRDXJsYfXhI1EwRC7G1jrn+PsxMda93PhH5CGaV6xcA+OY1ri8q6lSIjbUK2I3Oxvvy8o7W4y4v27s3mjckoBPjaH2zbHHYQ6L3BnRm9FWtXRoSveerWrvkq1q7NAQP2Uu2OgQtGdKIZojaqtZ9FcJhs9gsc2yfS2ysdwK+SbKxDkkPYWMdlB5CxjoED2FjHRS1Z5nn/msXOs1Va2B2ks4VQXlaFWyuReS317miuRn/nXXWRkWdBH3F1z8P0JsRFI0TkdAR6KRsjCqwqhtIkrYfDOC27C64osVg35bdxTbPzFrrY+KurSq14zPQyrjOQsyyjYO45o3ED0oAacaUJkLzyjhWzHFupodg3ADaKVDRfs7GtCEwoGrtw0FcVeszVsa1u0GMHonnq1KPaV43yGysea3LTOtYBeBnq/UUi72O5WMcJNNui36620JHWnAQ/bbpP3OyyKTJkGqIxyxNZHV8kqx+nkpmJIu04iKao9QvZ6fOjbMYU8mM58uFKre+qBlrteehDw9xifGQcjqCyla3UwgrENHfJ5Vx9iiZmmkioLX66zeZAnqhPi34vYTfk8Q81oGHAMBbf/eHEKWCvnSeFh3ZrxtKqZcppT6hlHpPx/wPK6XeMf/3HqVUrZS623zuQ0qpd8/nbj7YWx4V5RdXqdOAqrWq6VgrPUTw8s/6KkwUVb9Uhpff81F0YQ5jTWJjLSlVrR2bGNlY+6rW5hyNPcbbuB5v18aQTYx0vx/QJkY21l1mqPV6B1Sp2Vgz8uHSoCq1w1jvp/jUdghHyjhICOIxqMItjIP0rzRfoGYyjIe4NAQPKafm3ysWHtL9PsJ4iIWLhOAhVsW7Px4SFeXTvplrpVSqlHqgUurJSqmfW+MiXgHg8V2TIvILIvLFIvLFAH4UwB+LyKe1Qx41n79qjeuOitpTfcXXP6/3sWysk5BmBfOdOW++24Pxi/f5d2gwq8V8PLsLfvHeX4ObLu3mrS1jHZQWwnhIQDOZPezEGIKDsLG2xo5NjGysfVVrXaGbGI1jyVj7qtbGbfJsWnSx1T6W2rXWQkk8VWtdbKwPrZnMEC6bPmZdcXmsqfSP2mMx4sGXFbL24PAQ877iTo0usVkOaSazDh4Sq9YzLbCQrn+nVb2e9fMc6+8C8CWYpYO8WGS1W0op9VkAvmj+78Hz/78AszNuCrP7/0dDbpiI/IlS6lzPw5+MiJxEHVF92Tc/P2Cb097pzXd7ML7vo6/Fmy59MF5yr/n3VIkVmKgon2L6QVRUf0UsxFbfr5S/AeARAF4M4N8D+Bql1PuwMtN3mx+nMEvaVQBuAvCnAD4w/7cvUkptYVbh/n7t1wLg9UopAfCrInLtfl1/VFQfWVFOcyxigYcs2OukFIhW9U3KxqgCq7qBzJu5qGpmlBfRfKpqjHNRqmkgUFBqjnSIoBOSZjyEuWytiu3DQeyx3k3Rs4kxV3Y3RT5W7LnZvDnWuy9aaSEhVWsPDsJV6yyrl+ZsK6fKcmDV+oxWqeaq9RZtYuSqtc7h+hrEuKrYVpXawkW6M7B9VerMYq11ZjkMDwlJDGkzA9n8uhtRxsukLaZPv92lJMvbvahoL+6DEqlxH5SSYlsrqRZaFN8C8diYX3YpIyN6j6P29Gi+BQ6ymJ/ICGe0qL2deoxN7bJ2m3x51mNnDiWfzWbHX6zGxvPuQjXuZK8XeMjiec2xfDtFjizT/v4yXb5eFnjI4vUkRWq+1qaJ2alRi+Zb4CCLcToxuzYm1Jkx0VjrRVV6OZ7a7892jJ/5fHnbK5+OqJlE1KmuUHfJa66VUjmA/wDgm0Xk95RS/wPA7QAeBeCDAP4YwHvn/94DoATwdwBeJCKv2a8brulrAfwZISFfLiL/rJS6B4A3KKX+VkT+pG2xUuoazGICceWVV+7/rY06Vfqyb36+MQ6JcmIchFlrl1QzNwpqfuIooGLtMtY+uYy1TzYe0ntpu7HuqRAchNVmrPsqxFizQow1awgeEmKsWSHGmrXfxrrvsXZnxm5T4eOwXZ0YWUPSQ3bohbQb8OK4wDF9AXjITmFeT1kGnL8bgIcwGeWK6WPx+3MbHhKNta3Y/tyW99UtIoVS6kMAvk4p9RYA3zCf+iiAnxGR39KPV0p9zp7fSreeBEJCROSf5/9/Qin1BwAeBqDVXM+r2tcCwFVXXRXPmUcdmpJy/aeftalRv1yfsXZsYvTJtYnRp5BOjFFRp0lDujpyi/QQhWxqZPGmxhBZmxoDlE4Op6tjVJRLfV+F3wDgVwG8D8CbAXw5gGcA+A2l1A8CeKaIvHF/bmK35m3VvxLAt2i/2waQiMj5+c+PBfCsg75tUVEP/9YXAPrpxqmg1iq5aWniIXqVN6nEQCiSsjHSOlQtkPnxqqKYvkaW6EijEiBRZuMXVwGco/eS7ig+bo9uVa1H3QkhrTiIMYYZp5erZcTWohC3HOcUtdfSMIbXLuK7mrEYUV5N3hjXK+PVeJlpPR+PxrURIZZz9F5eLttmb85PlS9wke2sMLjeM1TF3ischJvJ+KrUdmfGqvNYRj7cnRmF5jydGY3oPd7UyMciSJm2vqHvnTUU0vl111BGV8tGFFK9MyOUUY0vJUU6/7tqKKMSP4vaW90fhaTGfV0iXcbttcX0bVP0Xlc0H3dx3Gly4/mwU4+N58tuky+fTzt1ZjzXzpcbRlINR/NdKHNszscXyxzjbPX37ZYZNjQEalJkxlmdQovmq6YplDbXFCmQr+47NU3QZDoekizPTM26Ni6nrM6LjIekUyzxkbQVHeH3axi6+bpYtWYJsHyfi1qpl7kWkXdixlzr+nql1L8B8FzM+OY3YGa427tXBEop9TsAHgngMqXURzDrCJnNb89LF7cBwOtF5KK29AoAf6Bm1bgRgN8WkdftxW2KilpX1m70gCq1jYcEIB5QZuU6IGzBZax9chlrn2zE42A6MTa5eefIuP+d1Was+8plrH0KwUFYIcaaFWKsrbUBxpo11FjrajPW3ccyHmKOXdF7vqg9V1dHXss4iCs9hJvHDMFDOD3kQtl/7YQq2kVAeoiy8JD+7yN72VwmyiUVsZAWrX/+CICI/DmAr1BKPRHAswHcAuB1QAAA133ZT+5xzCswi+zTf3crgH899Pqjoobo4d/6grXXJtUAPIQcw2wLY8/LC2CrWYeFh4RE77GCMq5Jo4AW6KyQTowsrlqHiM1z0NqAfGzWkNbkQ9YO0WlPPzhfbqy9dteRce2T1RI9QEMQjz5cdqxat2sWxXe6Xy9tGmSuFxKRP1RK/RGAp2JWYVYAvlEp9Vci8i97cR1RUcdFVz31BYBjt7kTD6nEqNSq0uzimJQC0arCql4hITMcZDX3VR97By4tL+Dxt92Ch9z5QVx3n8fgzZd3fO/khoc+PERLDOFNi+LBQ3Qz3YqD6GsduIi3E2NbmogsjhXCTrpxEABQebNEQEbjytgfmufmeCOvDFxETwxhY81V663UxEW2R9Pl2JdpzVVrHQkJxUFC0kJcVWxfldre1NhdmfYZ7ZCvdw3s75I6LqKb64ZCdjg9pJTEQD70+Rkqsrq/ZjiIhotQV8eJZMv7foaSaGhFkxn3vX4sz0/ETBYJwUO4a+PFamzhIfrz9kKZG+khm5QWouMik2KETBsXxQjpEg+hro1FArHwEK2LZbFKE0mmykr4sHAQHQHROjemBYykkdla88lxy0t+EFHd4o2+UXvYREZEGhH5HwDuB+C/YhbZd6tS6sWHsMkxKupIahgeYh7rSg/5qk+8A0//4B8ihUABuGJ6B55+6x/iUbe9M+j2Am5j7ZPLWHvXeoy3S76YPpcYB1F5fzxkg/AQjuJzaUgnRpex9ikkLYTlw0OcawOMtU+hxtolV9XaxkES57wuHw7iSgDxpYXsFx7CGxxD8BBOC5kQDnJQeAhXtEOay0T5JVBopPvfadWef90QkV0R+VkAn4dZCsd3YhbNFxV14nXVU00cpE+nr4UYB3G1QGcxDvLUD70BGw2ZpqbEUz/8hpbF5pCr1i75qtYu+arWIeI250FrA8wzi1nrEHHVOkSuTow+DUE8hqAlxxEPYS47RCEt0O21Q9JC1scyuCV6iLgleogqqyV6AFsd0AKd5WuJDsSqddR62hMspE0i8ikAT1NKvQjAz+zX9URFHSXpZjMtxHm6MSlMPET/kp+WYiaAEC6SVI2ZR60Zc9UILp+27yu+vLjDwEosWc1keFNj91o21jUZb1fmdZMR0uFoJsPH12PYxxrIB+MiKySkyc20kBkOomE3eW1cVqolhOSEf4zzykQ68sLEQ7SEkDOUFrI1Kowd92e1BiCAiYSE4CCAaYi3qGzHRpsrzxta8gQjHvYGSPNLSq5VtXmTYk71Y65S62aabRZ/90uDcqvF2S21FpgJIYSDmOkhWKaDAECBxJjX/4YCqdHIR288A8wRD+3+NOdy43EqJMVWR1oIAKSyWnuxGQfhIbojYDzkfLWBDY3536nM9BD9+bxDeMjFIkeuJYJMi5GRHqK/TuppSnhICtGy5zk9xHh9Tu1mMrUDD9Gflkk57Av+aVYTsRBLwfdIKOIhIreKyFPWWRsVdZz0kO9+YeccV0QSalbg2sSoKj62cc4DwG3ju7Ze1m3ju3Rej09srJsBFe4QPKTNWPe+XvqwrAM2MXInxjRgE+M24SBbAVVql7H2yRe955LLWPvkMtY+uYy1TyHG2icO4XGdz+CKdkEfpa7T4YyScKXZVfFmtIRxkKmjaj0EDzlfmRscd6r+eMhFwkOmAXgI51/beEj3/exLC+mzAfKdL4pVa59EgFpU57/TqnW+brxfKfVrSqkv6btAKfUQpdTLAPz9GtcXFXXk9a9/wDTWIZ0YWSEcNmuBh1x37jGYJMRlJhmuO/cYx2J31dolX9XaJTbLQ4x3SFdH7sQ4JHpvHICHWJsYA8zzkOg9X9XaJV/V2iVf1dq5lsYDAm1QB5h2voVD0kMKZ63crYkMwDQGsFJD8JCdAXhIPR2SFrL+YzSAlIoCInPdonWwkMcCeA6Am5VS7wfwegB/BeBWzNqiA8ClmDHXXzo//vMBvB3A44be4KiooyrjlKL2ppKW0rKT3cRDeOPfcq5aNYsBZpsam45mMosxANx4r4dAEuAZf/v7GEmNj4/viuvu+xi86YovXl24ozOjvYnRM9aGVsZ1QNXa20wmM8cW/tF0j+uxGGP9Zxk3xhiEhyR5DdEYAf1DYyMvjbSQzaxC3aweo+18ahxv4SDa+OxoikarbG6Ppp3VnxAchOVLC+G25no1mdNDfFVrc627IQx/J3NVpn2VoVR7ftciSFw51ug27jXESBIpxTy2hsKG0TBm1YimlMS4Pwqkxv1VSmI2k6FmM7oKGRlJI21pIWPtsmqVGMe68JAL1Ya5VvsDL1ZjbGun3s5X4954yMVijI2R3lxmhNxID8kw0vEQ/TVWmHgIpgmgjdU0gYw0TEc7NJ0q6Nh6QokgjIu0fe969/Nj1bqPZhsaIxbCCjbXInITgEcopR4N4D9itmHx+2E/PRWAHQD/H4DvPYwOjlFRByGrau0oJg7BQ+y0EPf4TVd8Cb7uo2/DJM3xjC/6DvPCAlqe+4y1i6VmsXkOQjy4wh1QmGMchKvWLiWEh2Tj/lXq7dx8MoRsYuS0kDMhDWLILHPV2iU21huOhjDWWnAzmf5V6hBj7VMa8PxefztrW3pI/+tlPMRuGNP98exLC9lxVK0ZD7nAyIcTD6H0kAA8ZLekv4+ay5RThx0hHGQIHtKnuUw01lFDtfaGxrlZfqNSagTgoQC+EMDl8+nbMGuV/nYRWX87fVTUMZPF9YVE7VlsdQAfzEZ7joeUKkXahDU8CcFB2Fj7qtbmnDkekh7CfiCkM6OFgwTgIdyJcTPr/3bHOMjZg4re81StXfJVrd1r3VXrEIXUyWpZ/3oYJQkhttg8h+AhbKyLgPQQNtYh6SFsrDmKzyU21heL/mvZWIc0k2FjHYKHDGk8E7XSaW+61KbBaSFz8/y2+b+oqFOlBz/9hcaryO7cbKaH1Hp6SElZzo73p6Tm5jLUTIbchpqfXq3SFJtVEZRNrVem+WzfIOOdKfP+4fvKlfCRtczrvo7mzPQQahjDmxr1auPYTAdJKC1kpKWFAECj4R+beWkiH1lh4iFZgUob66dSz2YTIy1kOzVRkjMpoyWrtVtJQWObre7aze/DQVxVa56z8ZDuHGu+NTlVmm3W2oWHuJ+TiWNt40kPSfTnDUwcpBAx00OAJQ4CcFpIgg1QWoiBeIw6W8ZPZGQ8LjM8pLuZjP5Yc+OZnWZsPG4zPKT7i5m+afliPca29oXwfLmBjXR1WfpzGwBqDV/bKXOMR6sv+btFhnzUnhYCGOFHM6Otx2VOzfQQ6+udJz1EH9vv18B7fy5WrUMUOzS2a9+i+KKiTroe/HTCQagA6ComWsdSSYzHuhTjIQ2PVz9XSYpU+leuXRnXvli+Lm4caOGuAzYx8rF2AkjnUvt2kLGWADyEW56PA6rUvs6MLjEO4jJDLK5Sh+AhtnkOwUNMy8NVa5dCjPV+KqTizQYvBA/hKnVIxjVXpV04CMtODyHEw/HC4vboVtXahYcQDjIlXKRybWqkOUVVa868NuZ6ZFpHraPIXLcpmuuoqH2Q/Ua+fifGhDPCHOKz9BVSZD3Ntd3yvPfVBuEgLIvDHtCJMSR6zzLWZJ6ZtTYOJWO9SXiIK3qPjfXZzIze23acq2ZjzTiIyzxbFW1P1VqX3QK9Px7CxtpXtdblM9a+qrVLzYD0kCLAeHNMH+MiLg3BQ0I4bBYb64sB32DZWIekh7CxDsJDuJnMGukhsWq9npqIhViK5joqag098JkvPBax+VWSIm2GbNmKijp9GsJpRx1PRWO9nhY511GmormOilpTi8LQAvFYjJnrSwuzkpuUK/Z6tVYt54xjKzOmT4/mWzSPWVScVQPoPSZUI6iSWeXa2ZWxRRYCon2T8OEgriq2hXgQh21F7+UrFH25dtFdcWwyk03G0Xsr9nyBgyzHeWOUI2XcGO349Oi9BQ6yGOfUiXEjL40Pl+3c7La4na3GZ0Z2Z8ayWT1ol4wmdhTf/GvcVlKg1tnqdGpsJNpKiuWxALCtVao3VGlcLnPY3DBGr0zbnRjdiSA6EsJVa18iSKZVqrkq7UsPCaliz1jr9uNriMFpM5ddihjVdr3r4+KkU7aM4lPG/VEgaYniq+Y/zy4lnd/fs66NWmwdsdcTyZeNfxZV6g19rYYEXWzGGCc6e50vx4tmMuOkWs5tatzaxWqMTW3tnVqnxkVayCKK70JFnRnLHBtWp8bZ9SzSQrI5e10UI6Ra1F41TaFGq3FTpMsovkVayCKKLykS6IRMQqx1OsUymi+h9+uoqP1QNNdRUYF64DO7OzGyrOi9g0oPWaSFJClGgYE9LmPt0zA8hC4rJC1kQEwfp4W4cBAWp4VwZ0ZmrXVxWsgl1JmRo/iMtZ60kG0HHmKnhXQba1aIsbbXmmOXsWbttbFe99iSKtohjWn2Eg8JaS7DzWRcUXvWWkoLuZNi+5xrrfSQw8JD/Gve96xYtR6iyFzbiuY6KipAD/iJFzo/xgMS0ey1gzoztv++SlKM9hELcW1i9K4NMN5RUcdNIZsaWSHxeXu5NsR4s4Z0dXS1QPcpKaKxO0zNmsjE93JW8DNaKfWdA6/zehH52MDLiIo6NAlHO81fRWkBsytYaXcF081oWspybVLZ6AjjIPpY78yY1FRdrmWJiswq13XvCD3rPZIbxmgFJKuZjK8zY9Y912Rw4yHa/OLzf/GFwtrUaOEhskRAls1j5ogH4yCKOjGm4xqNhoMAWI7HOXdiNKP3trTovcUmxmr+QJ0ZmdF626OpUck8MypQzTGBM+msor1AS9qi9wzUhPARAw8JrFpnqlqiJ6HRezoSwt+jMkY+qGqdOXY0WLhIYJqIXgVntppxEf3nEo1RXeeujyVk+XeWYv79ffCQxVkBjuUrZWThIfrjNpF8+bhy9N7FZkxRfLkdzbfAQ5rceH5cqDcw1qL2LlQ5NrVTcRe1To0XqrERy3ehzI21O2VudGq8WORLHGRajDDSYvmKYoREw0NqwkOkSCHZHPOaJmi0Lo1JodBk5tjqjkvfPf7uv8aq9VDFDY221vm6+GuYfeytc28KgMcAiOY66tjpAT9h4iAhVWorPWQAHsINY1yqVIpRYBOZpRzG2idf9J5LFh4yqItjAEpDOEg67n+/MQ7iAhCkHwAAIABJREFUSgux1nInxlH/tZwAEoSH9DDWXQox1tbaQzTWutqMdZdK+vt4belaS99YQ/AQX9fGEDzEit4LSQ+p1m8uw2khIXhITXiIDGku0/KyisZ6uGLOdbvWPRfzbAA3Bq65K4DfX/P6oqKOlahAGLh2QFIBGe/FhsY+GvL+OAwPWf96Q9hqltWZMUDjfP3Gs2cCOjGyQjoxsrhqHbQ2IOOaFemfwxGz1iG6UPdnq621AdF7rGF4SHyiRR0drftMfp+I/HHIAqXU3bFetTsq6tD1BT/1QsDYjb7CQYD56UZtN3rjwkNKoNYcRygeouMYqpZlRVk1AHR0RIByNMKomaeFNA7TzpsY6ZVqdWrUboMPB2lG3G1RrRI/uPMiIR0W4pFjhXiMW+aMpBExL0sby7gxOfVxY/zRCeEh2XiVEDLOqiUaAgBbeWngIJvUifGMgYdMl2gIAGxr+AcAnB1NjOSRrWSVJnJmNPGmgzQd8xuqMC53IymNtRuqNC5bRwdCcZBxQNU6o9aivHFRr1RzlToZGIapP+UbNJ24SAMxqumcJlJKY/xdOh5Si3l/TAPSQxgPmUhmbCidjbX5ZjXWURHARj586SGb2pe4C/XYSA85X5m4yMUqX+EhpY2H5Np4l/CQnSJb4iGTIkOq4R9VkRp4SFOkUNoY08RIDxHCQ3jckNt5/4/GqvVeKW5otLXOPfIYAG9cY90d87W3rLE2KurIiM+0h3T64uLhfuIhj/7ILfiGf/gzJBD87o3PxqM/ut5Lz2WsfeIPtDqgjGkhHiHpIdQghscucVpINu5ftd0c0InxLKWFhFSpGf8IWctRezx2yWesXU+VEGPNGmqsdTUe5MOYs9JDzLUuPGQ6AA/hTYrW2HH6h3GQkPQQbiZzntJCLgZsYtzl5jJF/1NWVlrItP/jHyva+yyZbWjs+ndaFVy5FpF1jDVEpMJ6pjwq6lD1BT/VP3qPzXPImfiEPFwIHsJpIV/9T7fgGe/+PWzUsxtwz8ln8Ix3z6isN97rIXRF7qq1S0Oi99gPDMJD1j8TPatar6mtfH3UYjuArbau95DwEFcnRu/aAScuD6sFekhsHytga4QlZqvD1q7/YtgZwFldKMc07n87JmS0qwC2WpHR7mOmY9V67ySIGxrbFKP4oqIcuv/PvBB6BwlGPFLCQ/QiWFIROlJymoiYa7UP41k6CF8WpYXouIhWbFWN4Jq/e93SWC+00ZS45u9vwI1XPrTjr4UFbnHKiGtTYzPCrF3XcqxaEj9Wv1Cai69b8BCrmQyniTSrY/X45WZMOMhYzGYzuawSQjgtJKsh9Wo8Gtdo6tWHd64lhGzkJUpOC9GRj6wwEJAzowLV/A5sax5TaWO9eQwAnE1XzWXa0kJqa9yOgHCF21e1zlSjoSXmt7+2TYyNMdZ+DsRB9HlflXpoC3S+fMZFFiqF0BGIcTsZD4GRFhKWHlKDUZHV4zJrLkNj7QuTjihNmgxjTgexxqvHtUlWa8/XG8vGMkB7M5lce+PZoGQRIx2kzJF3pIXwbS6KkYmHTFOoVEsLKVNASwSRhnAQPS1kauIhAPCBH346ovZWp7lC3aVorqOiespCOggPcRUTU6uiTQ1iAoqJjIMkLfsV77H7mda1Xb/vIzbWQXhIS2fG3mupABaEh4zXx0NGlBaSB2xi5OYxIQkgnB5yNp10HGmL00NCqtRtxrqvfJ0YXXIZa5+GGmv3fPffPwQP8aWHuKrWPjzEtYmR00HscfcLy9dMZseBh/iayXDVWhc3k5FyfTwkan8U00LaFZ+ZUVEduv/P9MdBWIx4hK3lzoz91y46M35i866t812/ny02h32zsQGbrQ5JD7Fbovdeapv2gOg9xkFUtnedGV3ydWZ0rqVvcGymXfJVrV3yVa1d8lWtXdpLtjpEbKzZPIdoSPhPSBdHlovD9un8kLSQQ2smQ8z+1H4PilXrqINSrFxHRXXIwD+4QQw1I+Cx/rbemh6iIx2Mh2ir2/CQrrQQAFBzNvWlD7waz/zrV2NTQ0MmaYZrv/DxnZk9evHB1wLdVbW2OOwRCPkwcRFloSOrcZ3BQG2a3OTLbVRESzHJBai7xzr+4WoeAwBZVqPW8BAzHaREWa8ehK2sQKGNz2QFCu1ByrU78+xo0tk8Bpg1kCm1cUlzdlqImQCi4yM6OrKhSveYvtHplakNqwV6/6o1G2vOtHax1XbG9TDjrddAa8s8ry67QWPcbrt5TLNneMhEUuTQGa/Vj5NmhAyUFkJj/QtTo90mRkc4LeRCvWEkkYQ0kzlfbRiJILn2XL9Q5si0U2s7ZY48XY13y8zEQ7TXWFmmVjMZ6HhIkQIa8tHU+nuqGrSHIypMsXJtK5rrqKgWff7Pd1etGf+wG8R0X6694bF/lZrnGA/RTeeN93kIVCP4gff8ES4tLuKT47N48QO/BjfemzYzzhXy3mh3Zuy/tq0zY++13FxmQHpISMZ1RhXtjXH/ii+nhZzJ+leaz6Trp4dwVTqkws3G2jB7Hvk6Mbq011F7fcXGOmQTIzeXGYKHTMREHgp0b3Dw4SGuBjGMjnCmtTM9hKrSnB7CmxqNyyU8ZLekv2Ha/WbgaybDmxrbdOt/jlXr/VBsf96uaK6jokhsrEOi9my2uv9aKy2k6v8hz3isagQ33uch+PTGJXjRW6/FTz30KXjnZZ/XutbOtHZXrXX5cBCX8falhQzq6hjAVg/pzLiZkYkdEL0XwmWz8XZ1YmQNid7zVa1d8lWtXdrrqnVfhcT0sdhYh+AhbKwnAd9g2Viz8XavNV9IIc1kBqWFkLEuma12yEoLKW2jF431/iqmhdg6suZaKfUyAP8OwCdE5EEt848E8IcA/mH+q98XkWfN5x4P4EWYnfn7NRF5zoHc6KgTId5dLsbpRhsP0ceNZXK1Y0tAL04pTgChD27Rm5pUYq01TTB/cs/mJtnsA24sVRBDvbxUxkESZV+Vfq064jHisTKRDsd9VXMzmdw91jd11pwWQs1kJBco7THVUZIkp7SQ3MRDxnllnLqutHSF7bwwcJHt1mYyqwdxqhmmS0YTI3nkTDo10kS4gYyOh2wnU2PMzWU2VGHM5xo/vaGqFpREGfO6zEYznBYCc+z4wE2hjAoxG29X1dpnrF1r2zYppgby0ZCRN/GQlJrHuNJDoFWx9cYys7XmF5OJmGkh+mtsIiPk1DzGHI+Mx7RUpjHVnws7TW4cy3hITY+Z/hy8UI+N9JBZWoj2XErN69Wf+xfL3Gw8U5i4SD0yHzP9NVcVKRItPaQpUqiR/mZBxYD18fiodSQRC2nTvn79V0rdUyn1MqXUdWssfwWAx3uOeYuIfPH838JYpwB+BcDVAB4A4MlKqQescf1Rp1Cf9/wXGOO0ZVPMQkPwEO4mzc1kXHgIr1VNNx6ym87M9UbVryrqeo+0YvmoIBbEYQ/ZxMhV6oC1whXtgCo1tzzfyNbHQ7YCWqCfGdBcZkOZx4ZVuN2dGV3ay02MIYkgQ1CSIXiILz3EpQl9gw3ZxMjJIox8uDKvfc1kLlTdVWs7LaR/1dpKCyE8ZOrY1Gg3kyE8pCXj+h/+0w91Xl5U1H5pv8+t3QXAt8//BUlE/gTAp9e4zocB+ICI3CoiBYBXAXjiGpcTdcrFxjoE8TgqeMg0nX1wceZ1m3wtz51rB3HY5thKD3GcXbbWhkTvkbHmzoyjvNt4s7HmtBCO4tPFxprTQs5wxqO+NnGbZZfxttNC6MuCAw9hY+2rWptz/TOtfRqCg7ii9ULXhpjnIXgIm+cQxIONtYulZrGx5k6NzrUcvReAh7CxDmomEzsxHooWUXyxQ6Op/TbXHwRwXwCfu0+X/2+UUu9USr1WKfXA+e/uBeCftGM+Mv9dq5RS1yilblZK3Xzbbbft082MOg763Be9wH/QMdN0Ubmu1+/oFxV1WBqSYz1EQzozRh0dxar1wSiaa1v7ylzPW57/4z5d/C0APkdELiilngDg/wVwP7QHjXW+U4rItQCuBYCrrroqvqOeUi2M9aJQlEyV2V1xalZydX56USxcjkuz4Qp3ajTWzouFzbzym1Rir9Wqwkkty9u4KDwucA3ViHEblQA7C+a6KdyJIFy1dmxyXF6/aLedWGsQa70YLyvN+pg57Q62erlpUevMaMX06dF7Y4Gas5vNItJrXoyWcWOwmipvjE6Mo3FlRfFV8/nNsRlbt5mVBpu6RdF7ehTfmdHU6sRosdc6P50WmM7vNI7l20oKFFpl02avp0ZnxprYapOf5nG1HM+6NJqstT7OIEar71yp5cOSKWKrqZ7jYq19mxhDERAfi724fOauuYsjR/Pp7PUiPWTBZpciRqW+EDFuRQm1ZK8XeEgyH5eSGPF4Exkh0842TCRbzi8q2osIvYvN2Dh2pxkbl3VRGy8q2ovxhXrDjM+rc4u1XowXzWUW7PWFamxE7V0oc2QpRfHNo/cWaSGLKL7daYZUi9orS5O1rqYjIJ3dN1LM78X5vCoSa69M1MEopoW069g2kRGRO0Xkwvzn6wFkSqnLMKtU30c79N4A/vkQbmLUCZHViXEI4jFkbR2Ah8wPXWIhVf8r9qWHuDSkmYy1Nihqj8YBzWRUbp7iH42JNXY0l/GlhTBrrYs7MTIesuWIqfHjId1oiS8thHERc45wEKpb5I6c6hBjzRpqrF0KwUWG4CEFbVguA6ryQ/AQTg9xdXG01lIMD7PWLjEewlF8LnFaSDUNrwN+6Hv+S/CaqPUkojr/nVYNqlwrpa4A8HUAHooZerEJYBczM3szgD8UkY8NvZEd131PAB8XEVFKPQyzLwqfAvAZAPdTSt0XwEcBPAnAU/bjNkSdDN33l54PpTducWxi9CnEPFtrA9hqFm9qXKhOUpQqdWMhA97/moAW6Nba2OQh6ohrCB5SBsT22WvX//IQYp5ZIdF71tpq/evddWRc+6QK+76KxjrqsLW2uVZK/QSAHwOwgdnJ2U8BmMzHdwfwVAC/qJR6toj89BqX/zsAHgngMqXURwD8JIAMAETkpQC+EcD3KKUqzAz9k0REAFRKqe8HcANmUXwvE5H3rvt3Rp0OSTL7IExKZZxeZDyEuy3qnRuTwozam3VTNNd2jTlqz1pL0XuqXh2vapprxKg+T0bZbENjDx/szbxOscQ4JFWe6D0zXlCP4ms4ai9rWasVjJt8Fbe3KKYZl6UfOxZznK2i9xbNY5b4yLgxo/iyGk2l4yH1Ml5v0fJ8gYdsZJXVmVEfb2udGhcV7GL+5DmbTYxOjNupiYe0RfEtkI+txIzWO5tMqLtiidLCRWZjjuXbUoXVmVHHR3LUy1O+G6qG/h0uU2LUcTeoap0pHR2xE0D0zOiM4uN0LGM/q9Z8eSF4SCm1Nb/4O2ddG/XYPjGq+hMRpJpx1zs1TiQ15iYyMsaFpEZnxh3JkS7X5kZM30XCQSaN2cXxfLNh4CFG18bK7NrIUXwX6/EyXu98uYE8MXEQfXyxpVPjKJnf5nJkdGmcFBmSdPX3VtMUiYaLNGUKtXiPniZLVCTq8BRzrm2tZa6VUt8H4FkAXo1ZnvRfikipzWeYpXb8AID/rpT6tIj8Ssh1iMiTPfO/DOCXO+auB3B9yPVFnU7d95ee3/tYrkqHNJexOzMGrPV0ZnRJCTBJc4zX2NDYaqx76tDwEE4LyfrfVwnhH6OAmD7GQVxpIaxt4o44LYSj+HSdTcy5DXpiuaL3tiimL6QzIzePcRlrFrPULmNtr90/ktGFh3AFu5TaOW8ea85NGA8JOHVe0ItwxxG1x5rQqaLzTQDiQWkhFwPSQzgtJAQPqShqr+nRXOZD1/xw78uPGi6JOdetWrdy/f0A/qeIPKltcm60/wzAnymlfnd+fJC5jorab517yfMMHIQ1CA9x5FTv59o2PGSS5mtF8YUoJHrPWjsADxmElgS0QGdtZOs/SGezbrPsU0jGNYszr8PW9v+iwQrpxMg6qE6MLM68Piwxax2iQXhIAFttrQ0wz6xJEVmx46jTzFZ3ad1X7jkAv9Dz2BsA/Ps1rycqal9lICBFssI0psrEMgo7LURP9Ug1JCQp6dg+eEiySgux1uoJILU9v/h+oBoAesVQZnjIZJR5m8j0wkGM+dnlz+bMzotW4gd1ZtSREAv/aMNF9O6L+WrsxUFyLNNCZuNVQojkDVB50kK0NJEsq1FWszthc1wa3edC00Km9ept92xmJoCcSaf2eP7NZYvmziacHjJFrXdXTMplpbMtLaTUWmxvqNLgfPW0EMDs3JcpofQQMZqoZIR8JFqVu60zo14xTpAY81yn5GP3Sly1TpWJf/Cx3KlRVw1ZVt9LaYxbyZ0aJ6Kgv9RKUctM8YmkVlpIqr049LSQ1Xie4kFpIZMms9JDOvEQwj8YB7lQj43OjDMkpH9aiD7eLTOM5sjH7jRDmnjSQpLVc0PKBKCEkH/8zmcg6qA1LC3E11FbKTUG8ErM9vZ9CsA3i8iH5nNfBOBXAVyCGZ78pSKyfvViD7Xuu9O/APjSnsc+bH58VNSR0bmXPM8YJy2bYrp0WHgI4yB9K9yzyvX6VctWY91Tvs6MzrXcIGZIekjevxIZkhbCCkkLYVk4iKOZjHW93CAm4InlSwtxVa03CA8J6czoSwthXMR17BDtZVoId2o0jgXjIdRq3GFQfGkhrvQQxkE4PWQIHnK+7L+WcZDdsv+bAaeFSGk//tFYH57WTQvp2VH7qQBuF5HPB/BCAD8/XzsC8JsAvltEHojZHr0BkQJ7q3XfoV4O4Bql1C8ope7TdoBS6t5KqecC+K758VFRx0KMg4ScibfMcwA9wGkhIWutztRa5XCSZk4sZEj03qDOjMxWD8FDAqL3WBzFFyKO4gvREDyEWesQuTox+sSsdYi4M+NxEFetw9au/7yahGxwIA3BQXbIPAd1Zgxogc7itBCO4os6serTUfuJAH59/vOrATxaKaUAPBbAu0TknQAgIp8SGfCC3WOti4U8G8CVAH4IwNOVUh/HLPZuCmAM4LMB3BOzE9Yvmx8fFXUkdO7aXzDON6tpskwLAQjZKJQT6UgpIaTRPk9TxkM8YzcOYprepJbODYaMh0xHOS7Z3e3HVCszAUQS07hLaqZ66HuzJGUcxGTAm5EysA29cMk4SE0NYiRbpYUAczxETwvREI/WtBBtXsbNWmkhADDOqhUekpe900KABR6ySgsptG8XbWkhxpjSQvRK5QwPMdNBmo70kC01NdNBVOlNC1ngIm1pIXor7w2tecxsfoWArJMWonPPOqax32khfDtWc/60EP0LRENpIfpcW1pIauAwWjoIUqP9/KQZGWkhE8mMcU7pIK70EB0HAYBMxz9a0kKMRjRaWggA4/kckhYCYImGAMC0HPnTQigh5EPf9kxEHY4W7c/XVFtH7Yd3HSMilVLqDswS6e4PQJRSNwC4HMCrROS5696QvdZa5nr+7eA/KqV+CbMc6YdiZqgvxSwW7z2YfdP4nyLyjj26rVFRg3XuWnOrgJrSh37h2ODowUFCCoJ2hTsg1YLwEN9es900x6aHue4jNvMhGddD8BA+4x2Eh1BaiARsYuS0kHHAJkZOCxmEhzjSQqzrHYKHBKSFWGsD0kJYvrSQg9rUyMbatanRlxZSOlATX1qIq2o9adx4yI50V5q5os04yI4jlseXFuLCQ3xpIbsBmxj7pIVEHbDELLK06DKl1M3a+Np5Z2ygX0ftrmNGAP4PzBDlHQBvVEq9XUTe2Ot277MGNZERkXcBeNce3ZaoqCMll9H2Kd3T6L3+a9vwkMko6xfFR4YohK22jHcIHsLmeQCXHYSHkNHmKD6XNnN3Z0aXGAfhKD6XOC0kBA/ZUmS8A74NMncdgoe0Va37ar8zrrvExvqwmskU1pbO/mLWOkSHlRYyLc03Do7ia1OsWh++PDnXnxSRqzrm+nTUXhzzkTlnfRcAn57//o9F5JMAoJS6HsBDABx/cx0VdZz0Odc918ZBtNOLeloIwHiIPy0EXYhHn/QQDfnQERU9DQSYp4Xo44ZMsOhzs8G0ZxSfXn6QRBmXpTePmc1T7J9mzEPSQmZ/kzmX0LgrLQQgtCRvwUP0tJDMTAjReXFOC0nzGrWGh+R5tWweA8DAPbbzondaCAAjLWR7xPhHgalWjTyTTjDVTJJ+Wp7xkO1kaoy3kqmRi6xXiEPTQgojaaQx00MgRmvvXCnTUGoPdkZpIL60EBAaclBpIb5jXWkhrqp1IULpIWZayEQSM11Fu+y2tBA7PUTDNLTHe6cZG8cyHrLT5ISH6DjIBjIN4bhQ5RjpeEhlpofk6ep6fWkhF4scqYZ8pPXq56IYGfhHXaRGWggAfOhbfwRRhyvBoCi+v4K/o/ZrAHwbgD/HrHngm+aduW8A8Ayl1BaAAsBXYrbh8UgomuuoUykbB9k7PMRVpeaqdAgewmkhXKXuqnDvprk3ii9EXNHmTY0uWZXmgHcgxkHCkkfMOysED8lz80Eb5/3xEMZBzoz6V6nPpBPn2CVOD3E1k2FxWkhmnQ7pVj4AD2GzHFLhHiIfDuJuEOM21q6qdQnGQcy/39UC3ZcW4trUyHOMg7haoF+ozGMv8iZGRwt0xkEuFuZ4WnS/GdRFxEFOouYMtdVRWyn1LAA3i8hrAFwH4DeUUh/ArGL9pPna25VSL8DMoAuA60Xk/z+UP6RF0VxHnQp9znX99zmwsR6UFhKEgwxICyFjrVeVp6MM46ZCIg2aHvxqSGdGX1qIqzOjLy0kyDyz8Q7AQzgtJM374yHbufnkCMFDtkfMVvdfy3hImHnm6L0QPMS8r7IAXILxEFdMH+uw8BA21iEVbjbWRQAewsY6pJmMbZ4DEj/IWLuMtrV2QFoIG+vCYbQXilXro6JhOddtHbVF5L9pP08AfFPH2t/ELI7vyCma66hTIWN3+TQ18ZBCGXiIZSb1lI4SBv7B6SEcBGTjH91jqyJsNYvRMIZG2rd5LA+YTT7mw2/Hf3j/WwAAr37ts/GSBz0BN97nIeb10OUYaSDKNO4yMufZa6ybFgK0NJNhXET7stHkJj6i3+9NLlAVjbWGMJILlIZ8QHvsVdYYaSHZuEKt4SDj3BwX1eoB3sxLahAzNfCRrVGJiQaUj7Q/4JLRBKWWSjJrHqOhJSOzYYz+83YypWYyhZUeoo9T7UHbUJUxN8NDUmNe/+AsjeYxDaWDoHczmQyJMZcqtTYewnIZb585dm1itCvcYlTXSzTGONHTQCDQv4eWYiaCTCQxxmzqzWMzGo+MxzSnb9ol4R+59uJgPGRM1QD9OcjNZO6sNozn8Ii+eOlpIefLsdEgZrfIDPSkTs3HrNZQkaow00Kijp4GbCs4sYrmOurE69wrfr5zTllVakoqcLRAt6rUjIc4Ks8+HMS1llucK94AOZ9+zIffjme+/dXYnPPWV+zegWfe8mpAgBuvnBlsq+DgOI3PxTOucA/ZxBi0lgpiIekhktOnwLh/lZpxkI2AjOutkXnsmYCMa04LCWmBbqWHBLRAH4KHhDSTYQ3BQ4ZUtIdsYrRxkP73lQ8HcVWt7eYyhF4MwUMcmxrvpLkLlBayU3WfduJ0kAk1k3HhIQvd+pQf8x4TdXCK7c9tHcy5taioQ5JlrHvsPl+IjfWQxI9Ba0PwEM0PfPd7Xrs01gtt1iW++72v7VhMOEjA+6WNg/B4f/AQi8Mm88xjQ2SsFXHZ2bj7jmdjzekhZ7NuTION9SUjZqu71/pwEJfxZmNtd2LsfpKysbbwkIDnCuMhaQCXPSSWLwTp8K2tQxAPjukLqPL5OjO65DPPLrGxDmkmw8b6fNl/LRvrqoW1jsb6aElk/Q6NJ1mxch0VdUJ1j53PtP9+t/33UVH7oZCqNeugNjWyhkTvRUWdNg1hrk+qormOOrE698rnmPF4RWrytdNkyTUvcBB9zFF7Xbz0oli4HJdm1ZfHaYklL70oHsq8epdUZtReUtFlaYkhS1RycVlUmPvE5l1xzxYj/YnNu64uQxbXDxOca+vUOL++BZK7wFOsqL2UeGmNtV5UoZfjER8L4qW1Y3NaSxx23RbFp7HWTSbLKD4ZN4DGVqu8gVhRfFqMXb7qzDjOS5OlbunUONHYaz2K70w2pc6MBXa18vsl2aQzim8rKYxYvjPpBIXFVuvs9SqKb0OVxrGzcdo9TqplxjLH8uVojMYn3KkxU6tqbQZlIBKZSgyEwsVeM3ft6tq40LrVaf1yfZsYuWqt/z12eoi7aq13ZlzEHS7Y9ImMDE590oyQaNc1kWzJdS8q2ov5nWZscNizmL72To2LTYuL27HT5M4ovjvLjeWZjIvz524+79R4oRwbnRcvtHVmnPPTO3M8JJkfPy0yJFrUXlmMrH0ZH3xSrFpHHQ9FLCTqVEjo9CJH8bkUErXHsmL7huAhdUA1TQQvfdDV2E3NU7S7aYaXPvBq99oBzWQaOovrwkGstYyHBLDUFh5CaSHcqVGXLy0kc0TvbXmaybg6M27TE+uSrH/0Hs/ZeEg3WuJLC+FOjbpyMA4iNO5calWwQ/CQ/eza6NrEaB/bvxMjq81Y95WvM6NzbUBMH4vTQu50dGK01no6M7pU9uCuo46ORLr/nVbFZ3DUidS5Vz5n7bVDOjOGGG9rbUALdFbbXrM3zDct/sC7/gh3m17Ap8Zn8EsP/trlZsaFhpzRCzHP9tr1rzcq6iA0hNPmqnWIQqL3WCHRe/ba9bsrXghgq1nTHi3QY9X66Oo0s9Vdih9vUSdO9/3NnzM791FnLzVNjHFSqiUukkyVOVcod7dFDRdJCxjxeEkFO7aPxovjk0rstY7OjHpx0Womo6WJ3Hifh+ATG5fixW95MX76qifj5nvcH/yZbxQfCQ+RxEQ+dDxkhnRQ1J5+bGpW25uRWt7utk6MiY6WjNqQj/mcBw9pcjG+5DRjWV7XYoPjAh+RvIFoKEmS12i08Wjn9EXMAAAgAElEQVRcL6P3smx2JQs8ZHNs4iGbmRnFt92Chyyi+M5QZ8ZLRhOrc6PevvpsuoriYzxkKzGP5bGOi2wkLZ0ZPZ0aF+NMNd5OjfrJlVyppTXNlLIQj3XxEABotOzFIVy2i60OwUEWl7WovpciSPUOlz06My7nJCGkw9+ZcTGeNGaXxovN2EBJGBfZ0fAQ7tJ4vt4wOjFy9N7FaryM37tQ2TiIPr5Y5mYUX5kt8ZDdabZEQwCgLEcxeu8YSXB8Ny4qpf4VgM8FsAngNgB/LSIX9uKyo7mOOlWyOjOWATFfnui9oLUBFW7uzBiSHgIBinT2Ms+a/rFzQEszmYAz8ZwVPgQPCYnas9JCAprJJISDjAJi+jYpPWQ7AA/htBBuLnPWgYcw/hHSmdGLh3DGozHnbibDnRp1sRkekh6yX8aa1cdY95WvM6NLvs6MLnFFO6TCzdF73JnRpYuEg+yW/W/zQu//pp8IXhN1cDpO9IdS6hyA7wXwLQCugNkxolJK/SmAlwL4XyLrgy17ylwrpe6rlPoupdSPKKW+Uyl17728/Kgonz73t59tjJsBbXMH4SEhBngP13IGNgBUyew+yBrPBR8WHhL+WbsnayVfvzq2OV6f/wlpgc4KybhmbQxglkIyru21A15Hh7QtaAgOwgrpzMgagoeEsNWs8wGdGVkhnRlZZWn/vdFYR+2VlFK/AOA9AP4VgB8D8CAAdwEwBvBZAJ4A4K0AngPgHUqph3RclFdrvXKVUtcC+DUR+Uvtdz8P4Okwet+hVEr9pIisD8BGRQVKzbGOpkiXPwMAisROCzFSPdSy4ppM3ThIWsBEPjQ8JKla0BFOADEue4V82OkgMDsz1mJ3blzMsbGWxZ89e5nndQ3V8Tk/SwvRfqHMy2M8ZNZ9cT6XKhPxsNJCCB/JlImWcDqIAw9p8tW4zmF3deS0EH2cCVS5SgvR5zA200KSrEZdrsajcY1qjoBs5OUSDQGAjaxCUa3eSreyAtOK8JD5WEdD/jd77x4uSVWefd9PVXX3nhkYjgMoiOABUCAoEk0+EzOA5xg1Ht5Xfb8oeCDGI2qMJpdHFF+NUYOa6EdEiDkYEwNKlIgoTEx84xc8IKeAIKIMiDOAzDCHvftQz/tHde9+1rOq1qrV1Xvv3jPrvq59zVSvWlW1e/fh7qd/634AYN/WPHZLhCNdwO6BTARZwK5h6X6fbN4wW2uTrlG53DeZN7stJj2jqUgqTN4cda1OjK5OjVJzNLDTQhQeUmUoE6gujio9JAUhl10dYaIi5rFsXGRSuRAQfY6BlQCiq9hj6ftBV61lWggAzKsunCGdGee5jXT4BN+Zdxb/DxTIh0RJduYdJJQb48ki4jFnHHfHwOziuHPQMRJAHujNjfGQXtvo1LhTbe/qtY3rmu9li3jIfLdlvl5HrQ7xqmKu9wVwDDPfVTL2i+HPNwC8g4heCOBRAL4/yYkmLQu8EsAjRhtE9BoAbwXwbQDPBnASgBcAuA7AuUT0nAnPExVVW7pq7VITPMRKDwnBQ6zmMvXfTCbFQ0aV68xXua5Qo06MVoOY5UkPCUkL0Upak+MhOi1E4yEurVPNY1zNZLT2TUx0RFep3XiIu5lMi6p/fystJAAP0ekhKbR5dqEl06tohzSICTHWWmXGWmq+4sNMMaZxEHdnRpeaNJfZqZrJPBCQHqLTQuZLqtRaP3r+O2sfP2oFxY6fGRIzv7rCWJft+0/M/HeTnmtar1BnA7gawKnM/BVmvo6ZLwbwRAC3AnjDlM4TFVWqh3/hXGPbwkG6Ae2Xp9mZsYnxDkCkq6rWANAbmut2BXNtt0Cvf15OlfEOoHCamGfrWA3wEHQmRwDmWpMzPPsGtEDXaoSHBLRAt+eGcftSTd5sQjjsaapJZbwJDlL1rUEdrVRnRo2DaNbapfkaaSFRs6vV0KGRiI4lotOJaF91+7OW4nyNFzQS0RyKKvbva/ibmReI6G9Q4CJRUUsqEsiHTPzgbmq8s8vmMcAQB5HNZjT+oRvCVOAggPlBXeMfsnlMcSwzIcRKC5FDAVVrjcf2h6RWNuiXBo+aaSFk/BKc2HiIgWmInS08JIO9LeMGhWEatBQeUoaLVOAhpc1kegoP0c1khsgHd/LF/wMAtQfIRXOZtDPAQGy3WgN0hxW3OU9aSFkzmdH2Pi0T/9g3mzfSQ/bJukHNZGRlc456VlrIaFujInPUNaqgc9S3jjUyei0aGNXVUDxEfg7TzWVSlSbSUsiHiYoQBhgYcyeVrlK7zLRexGjPrdZyVq3rNo8BihxriZq4msfs6LeN9JAHVHqITguR6SC7StJCZMOYpIRXu+l577Jui5pNzXqeNRG9FsDrAdwM4LFE9EZmvmQ4fA6Ar0z7nNNMC6kqtd8NYO0UzxMVZUhXrQcL1W9UPhxEV61d0sXDsPSQyRNALONdsohRaly5brBSEiuIh+gGMcuEh6QKB2m1JsdDXGkhWvtkaq4jLURLIx0uHMSe68ZDXPLhIanjz63NsU4PcWm5jLV/rilX1dpnrF1Va5+xnnd8haMr2rpBjKvCvaNvjj2gK9wBGdc6LWR+wb7maKxXjxirgrn+fQCPY+adRHQ0gC8S0dHM/FE0WspfrSbm+gVEdNzw/w8AeGjFfg8BcF+D80RFTSzdmTFEjTozKh/bpDOjrlq7ZIU6MKM3XNCY+aL4GnRmtPCQgFeWgXpvDTLtam5IbB8rHITa9c3znEoL0VF8Lu3TUlF7WX3zrHGQIOOd6Ki9+oZfc9cheIg21q0G72VNoveaKKQToz138msOidqb5lxtvIPmNujMGBW1RMqYeScAMPNPiGgjCoN9BGbQXD93+CO3/7JkvycC+O8G54mKqtQj/+n9SIQJ7C+YCSHsax4jt1UDGVmp1YkfZbiIUXDTKIm1LY7dNxvEmMki7gq3q2o9GuvTuHJdlRZSnNjTPKYmHpJnVJIWAnNczJUF8dLmMn21LTyivK8kKlJsVzeTAYBcfGvBnRws8I+kPUCu0kIGIk2k2xM4SGAzGfn1+T7ZAnaLTwTrsgXsqkgLAcz0jH0FKgIUGddGAkjSMwxWIjCMOeqhq9JBugbyYW5LheIhEulokdm5UKeDtMhsLiPNdAqyqsu5o0KsjXhQZdqFh6hz2oscq4/bpGrd5dT4+89zy2gQM88tIwFEXtZ83qpsHgMU0Xvy2IlKC5HYhmweA9gNZHzNY4xt1UAGAG587nvsXz5qdsVo1uZ3eXQ3ET2Gma8BAGZ+gIh+G8BnAZy4FCec1FwfXXKb9dGeiA4A8DMAl4eegIg+C+BZALYw8wkl4/8LwNuGmzsA/AEz/3A4djuKavoAQJ+ZTwk9f9Ts65H/9H5ju69wEFfGtQ8HcWVc+yrargq3ta/HPLukjXVVFDFTgj4l/sp1TTXDQ9TcgOJakyp1EzxEp4W0AhYxhjST0dJpISHpIbpKrZvLOOcqHKQdUKXWeEgaYGh1HnaTKvU0jbVrrs9YDxy/g89YuyrPvmYyrgYxekxnWrsWMermMTv0tqNKrXGQ3SU4SNTq1Kwz1wBeCsB44WbmPoCXEtH/txQnnMhcM/NPa+73SwAvn+QcAC4C8EkAn6sY/wmA32LmXxLRMwCcD+AJYvxUZr5nwnNHrXJpY61Za5e0sV6uToxWWkiA8Xa1QAcK7roucx3SmdFiqbV5drzCaLPs23bJ5rLrv9prPER3anRJN5MJwkNUMxndmdEl3bWxiXkOY6snx0M0Zq+j91wK2VerSeKHnqur1i5pY62r1i5ps1z1LUKZtHl2cdha2lgvV1rISLFqvUo14+aamTc7xr69FOec2fbnzPytYZvKqvH/Iza/AyB2g9yLdMwX32cgwr2FzEAxrGYEMi2kSyZ60TXTQqz3cUc6iMZFrLkaHdHIh+v9Vs+VQ1b0nuPVjYFekhWVa4aFhuj3fAP/IBPp4JSM8aSvJws8JCUb6ZD8uF6MpvEQmQCi00QUAiL/RgU6IpJIOmymh7TN5jIGV9QZWHiI0UymPUBfNJDpisfZXKuHBZHdu65d3UwGgPHV+trMTA9Zr9ND0gVjO6WxcVmbdA1DtVakg5Rty/tqjnp20ojarouHtCjHQDyY2siRGw9wiR2YVd82kWFc9Xge0E4dcGMdUj7jbTaTMaUXLepx/RSVZrunPrGGRO/5qtaJgw+3W6CbzWV0aof8MLW9P2c2nlF4SKY+4buax+zutpCq1+jrnv3eyuuOmmXNVuTerGhlestOX68A8K9imwF8nYi+R0RnrdA1RS2TegvmZ0TuqYe1Iz1EV6nTgIxrXdHWixadc3VVWs91LGK0cBC1bxlX3UtStAc2c90o41rjISGV5iZ4SJP0EFXR5oAKd6Yq2p32yuAhazWX5JCuaIe0QG+Ch7SVwdO4iEv6TWmlMq5dzWS0tJ3VVequ463Wh4O4qtaW0c5D8BCVAKLwkN16pbGc68FBXFXr3THTOmov0JJWronoMAAfAMDM/IolOsepKMz1b4ibn8jMdxHRIQCuIKKbmPlbFfPPAnAWABx55JFLcYlRU9YxX3xf/Z2VsSYHS61lGd7lwkN0pnXAXMvDDLd7SYaMfWkhaqreduSpWUiHTg9xvJ/6cBDHe7xttPXcTrVBsoy1YqtdeIg21nMKB1nXrn6waGO9VuEg6x3pIdpY6/SQlcJDWlXAf4m0zXR1cfQfa3lwkZCoPS1trHXV2jk3gMPWsqvUAXF56km3vV8/PUQb6zppIbFqvco141iIT0SUAHg0gMcCOBnAycz8W02OudRYyH4AzkBx10/dXBPRrwD4DIBnMPO9o9tH7S2ZeQsRXQLg8QBKzTUzn4+C18Ypp5yyyh8ie76OvfgcLFFyzh6rJ2/+Pg6e345n3PFdnHzPrTj/2Kfjm0ecvNKXFbXMCqlaa4VUrbVCqtZaq6FqHRW1V4tnP+eaiJ6NYg3fAwDORPF5edFIo0gM6Qxvvx3ATU3PudTm+scoTxZpLCI6EsDFAH6PmX8kbl8HIBlGrawD8FQUHXii9hBJnrq3kC1uLy5iHD3Pu2b0Hi0kJpctOjOOcJDRtu7EmC7AZKBFFN+I/V2cqzhsHcWXikiBRW548VjuqrVEQHxpITRgnH7n9/FH112MbBjDdtju+/FH1/4zwIxvHn7y+FdKVGdGMqvgnIzPPcJBFrdTMiPvFFudZ2P2eoQNj37PQcuM5cvL2Grd5bEnxsR5Bx0V+VfGXg9Za26ZHLbu1IhOjlyw1UlrgP4wfq/V6aMrxjqtvsFWr233MN8fV/7WtLrYNdzeR/y/2F7AvKgSrsu62CkWke2bzS9+fb826RqxfPtk80ZTkbVJ16hsrksWFrs8dlTXRh3T52Kv2zTAvHgyyK6NQFHFltXYFuWL1doUbHDYLWJIm56CMa+i+gaLY+SJ2qvPVvvkqruHJIJoHMRe1GhWreXfr6caxPiq1nJb4yAL3DJY6p15x+Sl8/bi9qjl+Siqrxgb/5IP9OcMXnpnX8wdNo8Z7b+j1wZJtrpnHmt3L7Oi9374rIBvIqNmU7P/WfSjAF4G4DYUYRiMosHhdSgKr38J4AIUFevrpnHCJTXXw6iTWskiWkT0eQAbARxMRJsBvBtAa3jcTwN4F4CDAPwlFdWNUeTeoQAuGd6WAfh7Zv5as98kahZUVK3H0qy1SzothHr1P2nrBLQmeEjqCsH1zA1qJjPc96ybL8dcrr7+z3s46+bL8c3Dy6vXFg4SsDLDQjyy+vezNVcjH44/tw41CMJDVFoIdHqIozNjR8XyrW3r9BAXHqLTQsx9Xc1l9sl0Wog519WZ0W4mUx8P0WMheIgvpi+gaedUFwuFtIcJidqz51Yba598ixhd0p0ZXZ0YtXQnxp39+nN3Kxxkd8/+faOx3lM025VrANsxLvTmAM4FcA7zmJUkos8AmE5eLWY7LeTFnvFXAnhlye23AThpqa4rapWoO/nbb4Nvz4OMtzW3P/nH/ypPc8j8/f7bk8lfGF0ctk+DEDcVFbUCalIZDzHeWiHRe1oLDTozhhhvLVfGdVTUCutMAH8G4JcAfhPAHwO4nojewsyXLcUJG5lrIjoURWfGxwE4HMAaALsB3AXguwC+zMx3N73IqKjjLj7HeKvqdTMjyW3QTRY5Bu6mJv7RJaNTY9JNFOIh8RDzvBoPKY3iG/4/1VF71jYrPESMBeAggIldWMZajG2Z2x+HlRjsLXP7j+fp6D25Y2p2W5R4SJ6ScV2leIj4vSQeMtBjrZLYPol8CFxk9P4vt63zKlxkhIeM0kJGeEjezgGJh7TZ+GBGqlNj2hF4SGuArqjGzXV6RtTempaJh6y18BCBeLTMKL51addo2LEuW1g0PkXXRhG9ly4Ypmht0jWqlU3wkDb1Fyusc9Q3xlrUx0BUX4sovvF91cZgMYovIXbG9CVkPGyRghc7OdrpIVMsLynJa7CeVroxjfLdcuFibqWF6IYxepHj+H7UFe2u2nYlhGhj7ata7xLdF3Wm9c5+x+jauHNgIh47eh0DB5Fjuyw8pGUlb17z27FqvcdoxrGQYYPBp4ibnj3s0HgeEb0BwFumfc6Jy3tE9A4U7MqnUDSKOQXAw4f/njm8/SdE9M4pXGdU1KJ6XfPNZhBQpU7Uvq5OjPZc97Zzrvo+Wcf2ueQy1j791TFPw3yi3oyTFv7qmKf5J6uq9ErhIa60EO/c+gEJVnoIqbSQtFNt6eY8zWTWhuAhKhHE1VxmrY7pmyIe0qbqqMGWGtM4SNthf3VMn/7ixNXVscEXJV4FPK2cxtonl7H2yRe951KZsa6rnaod6oi1riPdmTFqDxQ7fmZUzPxVAMcD+DaA/0Thh9dO6/gTmWsiei2KRYJfQVFiX8PMhzLzQ5n5UBQV7N8EcCmA9wz3j4qaSMcp1jpEIdF7Wo3wkBWaqx3CNx98Mj58wvMwn7SKFRxz++PDJzwP33ywzVs3WfDdBA/JG+AhDb7FLqrWE6rl4LB9Csm4tuYGtEDX6gRE7WnNOYy2T7oxSdDciWc2k69qHSJdtQ6aG2C8tXTVOmhuf/K5daL3YtV6DxKjePOo+plhMXOXmd+HIi3kywCuJKJziGh902NP+sx9HYB/ZOYXlQ0ycw/Fp4FvE9EXhvv/xYTnitqL9ahL3mt2YuylILHavK86M7LAQ8jCP/Q2jEgMiYfoxI9UpIOM54ptkQhi4yFcuS9Q4BGj7bIxKV21JjFu4SHDpIVvPuix+PUtN+G47Zvxe7/x1uG+qpmKSgcBESCOzYl5fM5o8Vo0HpJnJQkgVekhHjxkoDo1SuTDSgtpwzqvLOTmHVZzxdf47dxKD5GdGqmVYzBMo8k6ffTFWKs1wIL4NmVNp2ekh0g8ZK2VFqLwENWpcZ3alqkNa5JuEB4inaoPDykSQspNUosGGEg8BAMTB6EB5sVbS0tUsVMw5qG7OsIY17iIMRbg06UxD/0YJc20q9MiYFeizfQQ01hrlrpn4SLjbX3/W+khJQkhI9XBQaR25+MOijtEGghg4yE7+h2Fh4yRkF1WOkjL+nD1/We+H1F7llZ7ciUz/xTA84joqQDOA/AaAAc3OeakhYGjAHy95r6XD/ePipqq+rozYxAeorcb4CEhDWJ0c5mARYwuY+1TL8nQHkxWfdQ4CDdJAAmYq3GQoC6Oeq4jLURLp4dQq741W+PBQ1zaJ9PpIQtqvLriPU08RGdaz1H1eVsK/9BzdZqIOWberxoHabDOtlHF21WlDjHWPrmMtU8uY+1TmbGuqx2ezoxRe6lWERZCRF8kopcQ0X56jJm/DuBXAHy46XkmfQ36OYBfrbnv44f7R0UF6VGXmF27er36bz5ksdWTG++AbtMlLdAnj94LMd5VVeuRFtIW2nm5ubZboAeYZ92JsYnxboCHDPYyPGRNg1iaJniIyyz75GKpl3Lu5H9du2odIl/V2iVf1dqlJtF7O1TUXggeonGQMtY6Vq2jZkA3AHgbgC1EdDkRvZqIHjQaZOYeM3+o6UkmxUIuRMFS7wDwcWa+Q+9AREcAeAOKuLzY2zQqWHK1ea9npoP0F1JjnHspSPpnq1mMwD8WSCEfKiJD4R9OPEQhIOa+qhLX13MdDTICcJDixAIP0YdloEsZOnkPYID0d3jyjiXzWExmYxZOyTi38dV7SnY6iCM9RJ63aA4zHhu0S9JDKvCQgU4Laat926qZTJsx8pe5aCwDDJvLyA9inRww8JCBwEMG6ItmMq1WH/MKD9HpIbuHSMi6MjxEfEJYW5IWMjI++2Rdo9oYgofMUQ+7eLzvXNIz2mIXaSGJMS6N3gDjO7aFAQZQOIjRTMZOExkpRY6BMIxFOshYCbFR9ZLmuonR9klXnk08xN0gZgAXHuJuEKOr2FK6Sh2yqLEODlIlbax3WWkh5radDjKDZcuopdGMs9VSzPxuAO8moocD+F0Avwfg40T0XQCXALiEmW9tep5JK9cfAPBZFPEltxPRXUR0NRH9x/DfO1E0j/lDABcN94+Kqq1Hf+k9lWP9BfONiHVFe6H+wzpVOEiTBjFheIg7es8lbazrNJfpDivX2lhzQJVaL1pshIcEVKntubWnWgseB+2AN3wLD6lftW2Ch6wNSAux57rxEJesZjLqQakTQqRsHMSdJiJl4yDuZjNLJZex9u+rE0CqX4P83HV1zctnrEOay2hjravWLmkcZGcNPOR7zzi39vGjVpeIq39mTUT0MgBg5h8z858x8xMBHAngcyji+m4kouuGCxsfNul5JjLXzDxg5lcBeAyAD6FoITkH4LDhv9cPbz+ZmV8pu+BERfl0/JffY2z3Sjp71ZXuxJgsTM5WB+EhZVXruuf1VK1dKqtaA8BCkiHlHEnueCrqzowBxYhGeIj68w7aAVy27uIYgIdoDptb9e/nTMXytVr1/8DrFA4Sgodo7joED/GZZ5cssxyQNG2z1fVBDW2sV8po66p1iEKi9rRCova0GkXvlVSt6ypG7+1lcvHWM2iuAfwVEX2LiE4Y3cDMdzPzp5n5qQAOAfCnAE4A8LxJT9KoiQwzXwvg2ibHiIoqU1VBta8axHAvMR3lQmqZxJEShX8kXY2HoD4e4mkYIyvCuuV5SNVaG22raq2hUEldCFPeo+Kp3un1MJ8JzEFMMKrYZrCK3TAmIZD4PQR1M0wD8eAhxn0gkI0WuREPhYfIv4nVTKYkTUTiITDmspkWoprJoDMw0kMESYJWp6/wkAHmuyKJo92rNBzrWl2jCujDQ6RC8ZBccFNrkwXDuLVogJ0KF9lp4CJk7GviIH2j8YlOExmI2kpKOXqQ6Ehu2PQCFxEohnp3ttCRKclnnl0JIFYVOyg9xHwLto+dVI5p473LEb3nw0FcVWttrHXVenffTgTRilXrPVmzH7mndDKATwD4PhF9AsC7mXnHaJCZ7wfwN8OfiTX1GFEimiOilw67N0ZFBUlXrbvd6s9/3FMPX4WLuDKudTqIrkq7CoL2osX6c63raLSIURvt6n27ydBc5+VOPggPUTEOK4WHhDSXsSvcIXiIWaUNSQ+Za88GHhKSU203l6n/gNYLHkMq3LqivVxV6hBj7RvTxtrFUvuMdVUUIuA31rrLo5TPWLsWMZYZa2Nu177maKyjZknMfD0znwrgpQBeCOAmIvof0z7PUmT074diwePxS3DsqL1I2lj3uyFpIZqlbhC1F2KWNUriqVobYxoH8VStjTHl9zSH3aXiTbAsMcQy1upVQSMfxlxlrDUO4sJDtLEeqO0gxKMJHqKMtu7U6FKro3CJAC57pfCQtSqWLyQBRO/r6uKolaoHqYvD9mmaVWuX7Oi9+q9BjaL2lLEOm2u+bi4XDlJmrKP2Eq0uLAQAwMz/AOA4FBXqzxHRFUR0zLSO3wgLcWhVfUcQNRvSVWu92lxu59podxUeog2jwkFcyIf16HU0gLE4bAe3XKAkokFGN6BK7UsPYcc2jyvX7UHfyhezitZyKhESsT9nqmGMmmqkh3jwEPu+EvtqPKRVgnhIX+fAQ0rTRGrjIbnCQ3KFh4wfh1l7YMRFdtp9dMV6gblWDwtiWxrENVlvYjxkTdrDDomDJF3s6M8Zc3dVVEE7Sc+ots6p5jIt6lu4yEi60UyRDiKQD5UmIivTuvGMxkVK00OkjCSRyU26Xoio5UoI8VWp3YiHOz1koM8rzqUXLS6oKnaurkv+DprD3h3w9Y+vap2UOKmrnxGzDPYKzbCJdomZdwH4YyL6LIrmMdcS0UcAvJ+Zdzc59lJ1l12ld3XUSslexGi+2Tir1irD2s64duEh5nYQHuJpJuOsUlsLHt3bxpg21toslyyAPHZ7kZb51//1Ufzddz6M035xzXDn6g8hPumKdhAe0tZV6/rntRCPBukhYXiIaeKSgCr1nMJB1rSXCw8x54akh+hFjEELIBUOoqvWLvnSQ5ZLvug9qRBjreXrvOhaEKmNtb2IsbqarI11SNVaG+vd3biIca/WKqxcSzHzLcz8TBSYyIsB3Nj0mLFyHTXz0sbaqlo7pI318nViVCkHIXMDcBAty1gzcNovfoDfueu/inEAhy3cj7f86EsAgCsPe6yYrKZSffPcBA+xOzGquY73bYvhtqL3HHOVsbbxkGpDqI111ja3O+1qXEIb6zWZuR2Ch6xRD6wQ89yx2Or6D1Jf9J5LTYy31lJWrc193Wx1yHlC0kP0viFRe9ochzST8S1irKNYtd5LxFhtCxoXRURHo+jIeKL490hMwcMuhbneCuBoAHcvwbGj9kCdeOm7ET+PLZ1ecdsVaKs0zLm8h1f85ArTXEfNvEKq1lohxlsrpGqt1cQ8z0rVOipM0VjvXZrFPOsqEdGnURjpEwCsG978EwDXAPh7AD8c/jTS1M01M+coGshERdXW6E10wbOIMe+pqL3uODOOVPMY6plRe67ovZHvYLHtjN4T4zYe4q5aSyTEh4PoY8nKtK5S266dwGMAACAASURBVOkhxfYhC/ejTIcs3A8aDI2PrlInZHDbnIw7M46aySRiW8by5a0S1lpvD++DvE3G/TNQMX2avZb8tI7pG7QAstjq8u28ZX6LMego1lp1auSOYK/befE4HCppDYzHadYZGGx1p9XH/DCKb67VM75CX9PuGV+xu9hrzV2vSXtWF0fdmXE0t5P08cBgzGHPJT30BoIPT3oGyjBH423dxbFFA6Oi2qK+gTa0aYDe8K2lRX3juClyA6EojlXeqTFBbrLVJSa9V1F91miJC+nQ0lXp3NF5ETAREF+V2mogI8Z9UXt6XP5N7PQQs9K8oMLk5eNG4yF1ovcW/x9xkKjVp8egMNJ/g8JEXyuj+KalpcJCoqJqqaha11OuOzF2638164vec8k2x03m1v+I7zLWPkmjvbWzPw4tMdhbO/uVzrWi9pL6xsRCOqaIh7gQD2tuQHrIwNNMhjsBeIiK7es4mss0wUM0DqIr2q4qta5CN8FDNA6icRH3XIWHrAJAs0l6iMtY++Qy1j65jLVPvkWMZYpV671Qs//UXRQz/9pynGepFjRGRXnlM9Yh0XtaujNjiBp8e+5cxOidG8BWa1nNZIQuOOop6JKqiiUtXHDUU4aTJ7+vdEv0EOlFjVGzr5DYPq0mfHQTtCSkaq2lq9YhmpXOjEFzA6L3oqJWu4goIaLjiOjh0z52rFxHzYwWetkietFfKMFBpHTVupsIpMO9iLE05WM0VyGtpQ1jqvCQHnti+/S4Aw/xVK2Tfl45BhXLd9XBJ+HYbXfg+Xd/BwxgS2c/XHDkU3DVQScN87El/gG7E6M8vsBD8iwx903JRjoq8JAC9xiPDdpU0olRjAs8ZOQdRtuDVjX+Ubot9h+0zceHjuLLdafGtgMPaSs8pF0fD+m0+tgpqoS6c+ParIcdw4rj2qyLncIErUl7eEBE761LF4xovk7SX0RA1qZdw3x1kp6xPUd97OKOMT7q1NhWiIdGOuaoh3mBVCSUL1ZrC1QEYq4Z21fgImMVsW7F3JRyZ4W4rOJdt/bubBBj4SE6PaQaAdFVah2Ppxcmyt9PN5fRiSA6x1qOa3NsVa11p0bxlc8u1Uxml04EqZEQEqvWe6dWE3M9EhGdCODLAI4CkBPRMcx8GxEdCWDzEHGeWNFcR62IdNVamhCvSox1Xfni81xq0okx1dF7ARVul7H2zh3u+qN9jwDuBs587Btx55qDa821cJAp4iHuuea2bi7jnOvBQZzJI570ELjSQ9qe9BAHHqLHdHOZtVn1g9TCQ1Rnxk7iOK+Fg/Sd41K6gq1RksRRadYoia5ol+UlV6kJStKkot0kAcRlrP1z3cbbJZex9ika6yinVucC4E8A+BaAx6NY0DjSa1EsdHxdk4NHLCRq5mVVrQMUEr2nFRKfZ5230dwGZQAun9sapoX0yNFOvsGrQRM8ZNAADwnJx7bPO/lcl9H2SWdeh2htNjmzpDOvQxTSidGe2wAlaYCDNJGvah0iXbUOUYh5tudOXjvTVeuoqD1QjwNwLjPfo27/CoDTmh58qpXrYWbg6QAOBrAFwNeZefM0zxG1+lU0jBm/WbkaxtTBQaSSHokEELMToyrqlVeiZVqIGPPhIbIyrY/rq1pL/MEeM82FzryWrDXpirbYtTUozFEfieKzh4kfRGYL9YSM/TglQOEiSa+YwBkZ15VniR8P6Y3xEHn/DNp2Z8ZU4yM9OTbed9Ayv03I2yXbcq4YG3TMro15m83tFhs4iEZFZPa6Dw+Rarf62N0TaSGtnoGH6PQQGRG3Nutip/gqfy7tYbvAQ9akZiKI5Ic71EMvH4/pLo4tGlhpInJMogtz1DO6OLaoD4hqrPxAp5NFdNXaXuRYba41StJErsxrqxOjp0GMrGLrTou+3Gp5bG2srUWNGgFhiYeoSrPantcJIeJx5MVBerbx/t4zzrVui9pLtIqaxSjtBFC2uvcOAA9pevCJPlIT0flE9Hh124cA/AjA+QA+AOAzAH5MRG9vepFRe66COjFqlRnrmmqCePjMs3tu/U6MWi5j7VM7Ly66l4R/ntZV6Wmmh7jnmtshFe6QtBB7rsJDWgF/3wZ4yBrdxVHhIC48ZE59+rOayzi+htEVbW1wXXiIxkEs5MMBY3qNtaNq3WRxpFZIMxnf3OXCQ1zG2nteh7H2qcxYR0Wt0g6NFwN4dcntGwDMNz34pK8qrwTwiNEGEb0GwFsBfBvAswGcBOAFAK4DcC4RPafhdUbtIdJtzl2aavReQO8NqyV6Ay67mfEOYKsdVetTt/4Q/+/mTQCAT1/7lzh1q5mPrzsxhrDVdmfG+n8jO2pvhfCQ+slkthrgIW2H0fapCR7SCYja02qSFhLSxXFW5Ktau+e6q9Yu+arWLvmq1s65nqp1mWLVOoq4+meG9S4AzyaiT6HwwkxEhwE4F8DVTQ8+LSzkbBQXcyrzIvR5HRF9FcC1AN6AYlVm1F4sbayDqtZ1cJAK6YYwVoqHNt7OxA917B5XruUIwUGsa/bhIINqMy2bv5y69Yd4008uxdywcn1Idxve9JNLgZxx1cEnFfsncrKNf8hzc6rSQ8Tnc87IuG4fHiLlw0OK5jHlzWUGqpmMTANZ3FYIyMjn6bG8JD1kYjykNTBSb7L2YLFJUqs1wLxYFDbX7hlVwbmsj10CB5kTeMjarIcdvfEngrVZD7tEdvGcai6zJu1ih6hO9pJEjPXs5jJs4iAyTUSa67K0EDtNZHz39EiNjYcKxEOYz4RUAxlRqU6JnThI4qhq+6L1tCGWlWnNTttVa4WLiP31mNUwRlWe5WJLKy0kd5tniQ7tVhVuu2pdPXdejc33bbsQjXUUgFmvUJeKme8hol8D8BcA5gDcjCKiaAuApzU9fuMFjUQ0h6KK/RlhrAEAzLyAogtO7LEcZchnrEMWMWpjHbKI0cdhu89rvqKErBfzRe+5pI21Nt5SL7/jG4vGeqS5vIeX3/nN4Ylrn9bGQ9LJq9QheIheeKiP5T6vOlZAlboRHqKby7TrV3znsr7arv+gtPEQ80HpSg/xNZdxVa1tPGRyxEMnjYQgIC5j7ZPLWFv7BhhrLZ+xDqlSa2MdsohRG+u4iDFqbxMz38XMvwvgcADPQWGqj2Xma5see5ppIXdV3H43gLWTHJCIPktEW4jo+opxIqKPE9GtRHQtEZ0sxl5GRLcMf142yfmjpqcQHMRSg+g9jXg0id4L6urYIHrPV7V2iVRayIbuttL9Sm8vaYFeVyuFh9jGu/ZUa9+g3hsaBwnAQ1rKeM+16z8oNXft4rC1tLHWXLZL2iyH4CHaWAfNVWbZxXD7FNIQRpvnJokfIWZZy1e1dslXtXYpVq2jgrQ6metFMfPdzPyvzPwNZt4+jWM2wUJeQETHDf//AICHVuz3EAD3TXiOiwB8EsDnKsafAeCRw58nAPgUgCcQ0YEA3g3gFBR/3u8R0aXM/MsJryOqoaTn6/cbVK09OIiral1qllVDmCqV4SDGuEoPMfbtVzePKduWop6q6mmjrRc1ijuaBoyt7f1waImR3trerzDihs+pxj84SUyTnxAoFwiIqPpxKwnDQ8R9k2cKD+mQnQhi3F/jyQNHsxigMM9VH5CC8RCYOIiFh/QUHlKBPGXZALsXJB5ipod0FB5ipoWYSSJzWX+x0QxQVLFlc5m+uCPXJCY60kn72CHTQVSaSCsZP1A6SRn+YW7Laqw20zLWr8BDxH2F3Ega0WY6dfR1KCre9Yyszyxrcy2r2tqka/Osm8DIcR8eYrHW4m+gFy1qtrqrqtZ98TtqY63NsgsPWSgx1lFRI60CtnpRRPQ4Zv5ezX3nABzNzP89ybmaVK6fC+Adw599h9tleiKAiS6Omb8FtzF/DoDPcaHvANifiB6EorR/BTPfNzTUVwB4+iTXENVcj/7SeyrHvMbaUaX2Gesgs9ygwq0LgOnC8ixi1MZamt2RLjz8dMwn6o01aeHCw0+vfx6tZHLEw8JDAhAPXXgLwkNUVTok43ql8JDOVPGQgAq3RjyS+tfsq3C78rI10mEZawfy4WpaEyqXsdYKMda+uS5jreUz1q5FjD5jravWZfr+M9/v3SdqLxJT9c9s6ctEdAkRPY2ISs0FER0+TLm7BYV/nUiTfiQ9uuQ269WNiA4A8DMAl094Hp8OR5FJONLm4W1Vt0cts7Sx1lVrp2YUB3FVmrWxDmGrLRzEV7WW+ypjPao6X3XQrwAAXv/Tr2Bt3sWW9n648PDTF28vkx29V32/a2PNLfV1ugMP8XHYg86ejYdkmcZD6qdpaBxEc9ourdFsdVp/bgiHraWNdRM8pIlCEA9trEPQEl+V2jlXGeuQqD1fprVL2liXVa2jsY6ytEoq1wCOBfB2AH8LYI6IfgDgThTRewcCOB6Fv90E4MXM/B+Tnmgic83MP6253y8BvHySc9RU2bsvO263D0B0FoCzAODII4+c3pVFAQBYvTnJbV215l6AeW6yiNFTtbZkoCMlOIiQ6307BAex5vrSQrT3EHjIVQecgGN23omn3fMDvPSENw73rzYrJBGPJDGNu2omk2g2VVyIDw9xqUgLMVESJx4iePGBJy1kpfAQynL0FsYvufK5oPGQjsJD5rI+dgsERM7tZH0TD0n72JF3jO0HhFnrpTINpG8sgluTdtETfRVaSY4FYc6k6eskffTEW0gr6TtxEZ0WIqu8NlttpoXIluiliyONfd1G3GWQfZ0Z5bhvUaNru6cqzXohojbTvXx8Ls1S67l9Zcz7ucRD1HmUedav1/nsVR6jZlCrBQth5p0A3klE70eBFT8JhZk+AMBWFMkh/8rMNzU912qHqTbD7KRzBIqFlZsBbFS3byo7ADOfj6LxDU455ZRV8hBZHXrUJe81tl1Va8tY620XHuLBQVxVa9++rm/T7a6NOj3EUaX2dWLsBVTqBn48pHjxq/FGqTOvHdJpISuGhzRoLrNceAhl5t9k+dJD1NyQRYyJec0h+djTXACZBJTFlstYa4UYa60QY+2ba7HVg+q3eG2s6yxivOa331d5vKio1aJhkt2Xhj9LommmhayELgXw0mFqyK8B2MbMP0eBoTyViA4YoilPxdKhKVE15FvE6NQUOzEGJX5YXRzrv8k3SQvx4SBW1VqOaWO9WMGuzuMeT/bgIGn1AaaJh+i0EJfx1sba5rIrp3pREhceYhnrKeIhnQA8xOKygxAPnR5S/8mh57YcEX9aIS3OtVyRfj6FIB1a2liHdHW0jHZAXJ421rpq7ZI21i6jbc2NixijQrTK00KWQktqronosGGc3gUTzv88gP8EcCwRbSaiVxDRq4lo1LLyMgC3AbgVwF8BeA0AMPN9AN6HorHN1QDOGd4WtUw67uJzVvoSooY69d7r8LR7r8G6wQI+d915OPXe61b6kvZqhVSttUKq1tbckFajSivV1TGkaj1N6ap1VKxaR1XI0Z2xDi5CRE8nopuHkcpvLxnvENEXhuP/PxEdpcaPJKIdRPSH0/qVpqGl/ni6H4AzUHx+eUXoZGZ+sWecAby2YuyzAD4bes6o5hoZ6xG/1++rqo9mreW4qlKT3tastdj2Ih4O1tqHg7hYax8O4mKtvYsY1basWpPCQTRHTQPGxvuuw9mbv4o5Li740N42nP2zrwA5Y9OBJ5rzrSg+YYqSBNCdGvuD4f911F5i/B4u9rrotKjYaqtTYzl7PWiZf5e8XcZli7tH8NSauy6N8atgr/MWGygStxhYEJ0q27mxjXa+iD1RlhtdG9P2wGiolGU55gV73W71sWvYybGT9bGzO65cdlp99AVr3Un72JFr9rrYLovp64sqaDvpoydi+woWe/x/yWF3kj56A5OflnhCKxlgAcNrVrF9KXhxbDRXSnZmtJvLcGXXxqYyOzG6m8noBZHy9xsoU+5jq+VcCw/xbMuEEF/UnsVai+2FnpvDjoryasLPwESUouCcn4IC5716GJt8o9jtFQB+ycyPIKIXAfgQgP8pxj8G4F8nu4Kl01JjIT9GAYs/bInPE7VK5DTWHrmMtU/BixiNfd2LGJ1zmyxidBhrn0bG+8y7r1o01iPNcQ9n3n1V5VydFgKFh1jjQrnCQfZ0PIQVHsIBeEiqKtiZ4rLbrWrUoqPGdOJHCB7SVkiHq4ujlsVWN4jtC4nTWypj7ZPLWPvkMtahc3X0nksuY11X1/5O/CYyyqHJsZDHA7iVmW9j5i6Af0ARsSz1HAB/Pfz/FwGcTlQwjET0XBT0wg1NLp+IPjX8d78mx5FaUnPNzH1m/mnddJGo1a9jFQ6iq9Yh0lXrEIVE72k1+PbcuYjRp6BFjFoV6R8behUdGituD1VIC3StkEWMUdNTIzwkwHhrpQ0Qj2SF4ghCjLdWCFutFdLGXKsJL11WtY7GOmoJVSc2eXEfZu4D2AbgICJaB+BtAN6L5vr14b/VVadAxe9/oqau0dtgXzWE0VVraOMtzLSuUltVa5UQIivTuk9FSMOYEBykmCsiwgJwkOLYEvGoj4MAAEmcxJEWsrW1Hw4tMdJbW+vLDTmR0YmRk8TcLx2Pc5YY11XgIQL/cOAhpV0bZxAP4QxIF9T4CA9ps/G45BYbHwgLXEQ85ts5ePScyHL0RSyfhYe0BsbX9e1sgPlhNF+7Zcb0tdMBduUmHrJTbouoPh3T104G6IpK5lzaM6LcOmkf/SEC0i7DQcRbSBkuMoru0xVtHdOXqOg9acRdY4vjE0pXoiXW4RorGzej9nydGKur2LoqrSvNGgHpinP5ovb0sbri79XtRTsQNZk8n30PJqLviu3zhyltQL3Y5Kp93gvgY8y8gwJSrip0BRF9H8Bhw3jmHwK4jpl3TXrARs8mIjoURWfGx6H4dLEGwG4UcXjfBfBlZr67yTmiVo901doph7H2yWWs/XPVdoNOjGGIR7Wx9slprD268NCNOPvOr2KOx5845inDhYee6p0bkhai5cND3HNnAw9RzfacsX0WHuJLExGy8BDV1bGdVaMW7VQljSgcRKeJGHOV4dUVbVdzGY10WOkhjkWMPhwkpMK9VMY6dO5y4SEuY+09r8NYV+m6Z0+jKBi1l+seZj6lYqwqTrlsn81ElKFYy3cfgCcAeAER/SmA/QHkRDTPzJ8MvUBmfisRHQng3wGMunyfSEQM4Hpmfn7oMSc210T0DgB/AmAORQuLe1F0uZkDcBAKCP3PiegDzByXGe/hOuaL70N5M9FCVtU6QLpqHaIQ460VEr1nzQ0w3vbcyQ2DjuLbdMAJAICz7/wqOtzHltZ6XHjoqYu3m5Mnv59XCg8JaD5nnzekE6M1twGmkE3+93Vx2D6FcNjWeRvgICEctlYT491ETYy3Voh51pqEj16cGxcmRi2XJn9aXg3gkUR0NIpOiS8C8BK1z6UAXoYiOe4FAK4chln85mgHInoPgB2TGOuRmPlnRHQaM/9YHHcdAL3yv5YmevYR0WsBnIMCLj8PwH8xj1dNEVELBaj+BgDvIaL7mPkvJjlX1OoR56L7oviKO1cNYfQiRlciiF60qI223Zpc/N9TpXYlhITgIMV1cOWYr2otkzR0VVpz2Fabc7m/I+N6fBuAnC2UBIncHwUe4koLyZLFc+sujtwy8RDOEpD4nfMsWbwP8vbkeEhp18aaeIiVBtIy8Y+8DZBCSQz0SCRK5G0GyS6OLXPbShORd3OWYyDQkaRldnHMWgMsdMfbA/Eca7cG2C1ykDUe0s7MNBHZqW8u7Rvb7XRgpIm0k4GBJ0hz3Un76Iq3jkylhXSSvoGLtDg39p0XaSF6YaI24q7OjBoXaSLLTIttXzMZbZ77Bh5i7mt1U9S4iDDTGg/RVWpdeZZIiFWl7rur1t2e/QHgxue+x7otKspSzci90qnMfSJ6HYo+JCmAzzLzDUR0DoDvMvOlAC4A8DdEdCuKivWLpnPhpdfzY7W9E8B3JjnWpB9tXwfgH5m59JccGu1vA/g2EX1huH8013uojvmi+cWEZq1d8kXtueQy1j6FdGK059bvxGjNdRhrn5zGukIb778eZ9952SIWcmh/O87++WUAgE37l1Svy6TTQhyJH1p6X1daiJYPD3HO9eAh7rn6Otzb5pgbD7HSRISSluri2NJpIvXxkHbmThNxzk30dvXczIeHJA4cJsBYazXBQbRcxtonl7H2yWWsfXIZ69C50VhHNVaDD7nMfBmKniXytneJ/88DeKHnGO+Z/ArGIqL/AvBkZt5ORGejIDH+fHgNQZr0u9yjAHy95r6XD/eP2gvlq1q75KtaO+c2Md4rFL3nq1o7VVG1PvMXmwzeGgDmuI8zt2wSF6kvJKAFujbeIWx1WxnvlcJDGqElDdJhGuEhDRrRNMBDXCbdJ23EQ9SkM2MThbRA1/JVrV3yVa1d8lWto6KiKtUZGuvHo8BTUhQNCoM16bPu5wB+FfWatDx+uH/UHqhH/tP7IRfzDpSZlj23ueeuUlNfmSvhW7TR9iEeIfu6zHMIDmKftz4OomXhIIOB2naYOjG2obe9dJcN/e2gEZ8gDs1EMO94APLcaWp+CEhlY5nUSgeR+3KWVP7OK4qH+JrH6HEDLTFxEGNbp4lkDBriIdxisFiHQO0cuaggJtkAfTGetnL0RJpDLpGOVh/zsolLNnCmifRUGog0bkV6SGrMleMaFZH4R5aU4CHiz90SBjlTVeqUGAviE5I24rLKvZSxfK4GMrrSPFD79q2EkPFcCwfRiIfDTFuV5txd8R6Ix4ae2+vrqrVtAW563rus26KinFqZpMylUJ+I2gB+D8CfMfM/EtHVkxxo0sr1hQDOIqIPE9FDynYgoiOGqzhfOdw/ag9TYazH0sY6D8FDlLHWiSAuWeY5YBGjL3rPOdcTveeS1ZkxBA/xdGaU2pqtD7rdqVQ1AApID9kT8RD3XIWDZAGPDYV/pAoXSdPqv3dLzdXIRyt1oCUaB/HgIlLaLNvpIQ48RJlll7FeSvk6MxpjAcbap2lWqXs1EkAWzxOj96KmIEKz9uczpo8DuA7AswD8y/C2fSc50KTPrg8AOBLAWwC8mYh+gWKl5wKADoAHAzgMxf3+2eH+UXuxfFVrl3xVa/dc97ZLKxa956laO6WM90WHbMSbf/5VtHh8jB6luOiQjdZU1jhIwEdv1sZ7mfAQy3iHGOAAlto/t/5jQ3PY5OCwtbSxDkkP0cY6BPEI4bCt8ypjrY24S6Ut0JdB2liHsNRavqq1S76qdchcXbUuU6xaR02k1WeiS8XMf01E/wwgZ+bdRPRIFCklwZrIXDPzAMCriOgTKFZuPg6FoT4ARc719SjaVf4jM18zyTmiZlsP/8K5kDiINxFEPvkss6yNtzqZgYeYQ96qtZzray7jMM8+HMQ514ODuKrWFg6i99WLHGUUBTPADGZzH+a8mKfzsxPxdyEycZGUTDwkSYxmM8bxlxEPMeb68JAWVUYz5lkJDqLSRIzHFusxhYcsyLQQmGki8u+hkkTQypFLXCTL0RdpIvLDRJrlmO8KlCIdoD8YH6uVmdttEZfYzkxUpJUMzAYwCgexq9oKJclNPMTYVuZaLlzUVeoW5QZqohcuSnOtK95NpCvRuootFz3qqrQ23rqBTF/sb+Mg7gQQOa6r0vo8XWWe5d9eG+t+SYX7R89/p3VbVJRXq7NCXSoiugPAjQCuIaJrAFzDzGdOcqxG3wsx87UArm1yjKjVL++iRc1hC/mMta5am2Nqu1F6yOpfxFjGYZ+x9d/QVmWFNhhn3PMtbFp//PjGpH61eFWmh1hza09tVOFma27ANxhqwaNOE3HJh4c45/rSQxzH0lVpl7G2zlsWtWdsL807eIix1gox1lohxlrLZ6zrNIiJioqy9HEUTWRuQdGk5tNEtBnAVwG8k5kXXJOlInQVFayial1T2livFA7iqVobY7O6iNFZ4VbXNKxgb+hXL2gcX5T6myg8xMVWa7McgodoY71ieIh6FQxpLtMED4HeN8A8p8p4ZwHmWXd8DGnyEsJha+nzhKSHaGM9zaq1S3YnxvofBrWxDsJBNMMdwlJ7cJBYtY6auvaQyjWAlzDzY0cbRPQZAL+PokvkxwC8pu6BormOChYPxFfcfQ8OklPltrWIUZla/f4pt619PcbbNTek0jzVRYwBVWrLWOttVteRF9sPJGuwX77bOt7WbP14jjbm0lyniWHcmciomLvsr4WHZImx7bIpU8dDrLnjcfnYGJQ0l5HbbDWTMfdNrWYy+liimUwu7tcWAwL/4FZupomoZjPyOZi2cvTEJ4Q0zbHQNfGQeZEgIaue7dTGQeS4xkW6+dgQ62SRLMmBQWaOj399o6qdWfgHQz5KfZnXrpxrX4VbV6bNMfUBTz3C5X2jj+Mz09Iw91VMn89M9xx4iDbTuTq23B4M6n84iIqqrT3HXG8jolOY+bsAwMzXE9ETmPn1RPT9kANFcx0VpIf9ffXaVMtYByxitA1v/aq1jZKY2y7Ew4eDhHDYjRYxauMdsIhRN5cZGeuN22/AmtzOvu8hxUUHPaniYAF4iK5ah+AhrdnAQ5ohHvXn6n1D0kMsPCQgH1vjIa6kEWuuMriupBEtXdEOWcS4Goy1Voix1gox1tZcta82z3UWMd7ywnd494mKcmk1MtdElLBekAS8CsDfEdFNAH4A4FgAI1cQ8J3m5FF8UXuhtLHWVWuXvJnWDk038aP+3KXsxOiqWntxEFdsn6iGnnHPtyzeGgB2UQub1j+65MTqb5Iq8+ww3l48xGGem+Ah2liH4CHa8Op9Vyo9hEPwkICYPq2QmD5rrmWe659Xc9ghLHWTzowuY+2TNrghx9LmWVetQ84bgodoY11WtY7GOmoqYsfP7OoaIjKqTMx8C4BfA3AJgPUowjmeRUTrAHw+5OCxch0VtYeqirfeN7yTa5RSk66OIVVrrZCqtVaI8dYKMd5aIVVrLVcL9KWUrlpHRUVVaPZNwhdn3QAAIABJREFUdJUuA/B1IroYwB8y810AMKxmXzL8kQpYbBbNdVRNHf23/xuSsGVVBZE51r4qtc1aj7etxA+1ENFVxQ7Zt9gWsV6BixjleOgiRlm19uEgrqq1hYOoaL0q3vqBZI3NaAPmbURAX3ZmTIyYP06S8bVkdtSe/D1YdXWU7DW3zFi+PEuM+8/FXru6Ni6OV7DXxXEg9jUfA9Z2hkV+2uriqLczGFF8MtYvb5V0beyqbRHNJzs5Usvs4kipGdOXZIxcPC/TbICFbvESn6a5Ec2WpTl269g+UVHNknyxSurjsNMkN2IbZVfHjEwOW1e4y6rY88OvEHxpITYuUv0O76s0SzPtSwtxsda6Kq1xEQvjqGDhAbtKrY8tK9N99Xqsq9SDEjzkxy/6E+u2qKi9Rcz8diK6AMB5AG4monMBfISZA77frlYwFjJsDTmRmsyNmh25jLVPLmPtUxM8ZI9cxOjSMOO6csx5Yg8eklT/vfcEPMTadpQgfPu6kkd0BdvaDkBLEjU3zQIY52xyxCNV+4ZE/jXCQwKMtU8hVWrfIkaXXMbaJ5ex9qnMWEdFTVOrtUMjM9/CzM8E8L9QdBO/kYieOY1jT8Jc7yail4ROIqKDhnNPm+CcUSuoomo9mULYaq2AJnD23AafPUPMszU3YBGjVlAnRq2Bfd71FfjHVLGQgIWIWiFdHKNMUQPEI2uChzRAPEJMu1Yj89yAtXZlXPsUYry1QthqrTqJILFqHTVVrU7melHMfCmARwP4AoAvE9FXiOh5RPSwSY85CRZCmPwuiyDbKtNRn/sgDBxEJ4L0yfyjiiq2fh/WuIgrEYRCEY9+/X1TK+VDjOkqdd9dtTbwkDqdGGUTRImHBC5iNJAQbayZsXH7jWCUP+GMGD4tIhsPkcdPk8VzG2gIEIyHyIvTXRub4CF5RkgXTFwkVRjHaP9Bi9yIR8t8fMhOjr59OQPSBXM8HeIiRUyf7OJo4iJ5CpBAS9DKFzs5csZgYb4oYwxkPGCaIxcoRpLli4YrzXKjYpomubOrY5bmi1XTlHJ0kRpjEgfR+EgqKtOZQke00daVaDlu4SBLWA6TRtyqUuuIO1SP630HOi9bIx/iPtf7auTDWqgoo/Z0NOogvuVGLb1mvUJdJiI6GMCzATwKwHHDn6NRFJ1/HcBvAVhHRNuZef/Q40/KXP8tEf3tBPNW4Z8gqkpWVToAD1nKBjEuuYy1/7yT4yGNOjG6jHWFzrjv30u/lsoBXHTgb9Q7rwcPcWmlmsvkmTumz7UQcZp4CGs8xHFejX9YyWuO9BBSOIiuaLsWQGqkw8JDQmL7FA6SBqR6uIz1ciqkwu0y1j65jLV3rsNY19VtL4lV66gpa3U6u6sAHAHgPwDcBODLw39vZuatREQAjgHwmEkOPom5fu8kJxK6reH8qGXSURd9yACHrLbmAXJlXPvUDPFoMLc/+SuGqxOjT65OjF4Nq86HVCSFEIBN+5bE8AFhGddaTfCQBnNDujhqDRrMbZIWEtTFUalJ0oju6hg0V5nlEONtLWIMMM/LWbWW8lWtQ6Qr0SEKMd5aZVXraKyjohY1APDrzHxT2SAzM4Cbhz/BCjbXzNzUXEetJg07KtoNYjyJIOKF3dcgRndBNhJAAnAQ71wL8TDHJVoQgoMAJhJSioPIbUeb8yAcBDCRDTFWhYTkICML25S4PVF4SJJMFw8RaSF6XwMPaZfgIkbih0JFxN8tz8rSRMp/32Lf8UiuOjHq9BC7M6O5TSp5xECeRJfSvMUG/pG3AOk9OeVFHKTYQSS2ZAwWaAllOViawJQxEF0epdlKshy5qKAmKWMgujqmSW5USaWZTtPcxD+S3Fis11LmWVbI0yTHgni+66q1Th6RZtpnrEOMtzc9xIGH6GqxrmIbmIZeiKhxEb0tzLSVAKLPq8yz7DuQx06MUculVcRWSzHzRBXpuopRfFGlOuqiD9Xe12WsvXMdxtonX/Sec67DWPvnNujE6DDW3rme6L2RNj5wY+XiBprkVVCng0wRD3HJwkFCujjOCB5i4SKOX9/et356iO7iCDU3cVSak7TE4Aq5qtQWWuIw1tZ5y4y1HA9qLjO9d/cmeEgIpuEy1t7zOox1lW7/vbfXPn5UVF0R4mK6MkVzHWXpoRf8KUiYAF/V2iVf1Tpsbu2p3qq1c66nau2e665au9Qoek8Y71ffc2Xli93WdF//sZL6fyMrlq9BC/SVwkO0EQ8778RTVwwPWalGNC6j7dOKmWdP1dolX9XaJV/V2qVYtY5adq3CyvVSK5rrqFLJr5CNSrTOqdaJIHqRo/gK3Ko0+yrPXD3mO5Z5XJ0AYo5bVWt5Xh8e0s9NLp3kvvVxEEshOIjQxgduxPq8PGqPAVx0wBMBdpgcSkxsJCHDuGs8RP61Q/EQ49oUHpK3EuP+Km02U4WHZCXNZCrwkDyDjZY4kI5if/F/R9JIWVqIgS5L9CADErEvt9jAR3SzGaNymTEgsAzOckA+fzNGLjAOab6SNEcuk3Y0LpKwUVFNkvF9laY5ejDxELkt9x2NL/7fw1JrI04Oc72UMX0sxi0sQxtvhxHPc7dJ11Vrub+eq82zVbXO7d/p9pe9zbotKmpaWo1pIUutmf6IS0RPJ6KbiehWIrK+0yKijxHRNcOfHxHR/WJsIMYuXd4rX7166AV/amxTwCJGCw9pUKXWUXzOuQ3wkBVrEOOL3nNJG2thfs+47z8qq9bbqYNN+zyq/nm0VggP0RXuIDxEN5NpB1QtA3AQLV9aiOtYGv/QVWorTcTYVz0mdYU7cZhUNVebY70tZSEdDmNtnXcVGGufXMbaO9dhrH2KxjoqajY1s5VrIkoB/AWApwDYDOBqIrqUmW8c7cPMbxL7vx7AY8Uhdi81sL7XyVO1dimk0mzNbRC956taO+c2wUM8VWunJojeG6kqJYQBfPqgU92TSb1RrxAekgc0l9HmOQTxsLopBqAl9tzaU+25Aa/ClrEOwUO0WQ5APCyzHDBXm2VdtXbJZayXUtpY+6rWLvmq1iFzI/IRNZOKlWtLM2uuATwewK3MfBsAENE/AHgOgBsr9n8xgHcv07XtkTrq/A/DaAmjK9Gy/4c22sq0WlVsY665bxAeEjpXKLUY7vrmuRQHEXKZZxqY6Aj16letvTiIGq9KCWHAX7WWuMgK4iHyg4kPD5F3bFlaiAsPIfmVfws2WuJoECMfkxoP4QwgBy6C3NzXSBrJzOYxnLGJZQkTzykDshFNyiBpvjI28BC9QNL4IJKwjYuIfUnNHah9JSNsxedpUy8TQKwqNSr3LcbF3Cm+o+uFidpcSzOt+y/lHuMtDbI21qzNs9qWeJ5lrB37jvTTl/+RdVtU1NQVzbWlxuaaiF7q2YUBzAO4A8D3mbnr2X+kw4dzRtoM4AkV1/BQFJ11rhQ3zxHRdwH0AXyQmb9U87x7pY46/8PmDQEJID5j7eShPeY4CA9psojRMs8B1TVlrHXV2jl3SosYAeAPfnHF8qza3gPwkKAqdQM8pBlaosxwyOJJNVcbawsXEdIVbW2sXXhIiLHWCjHW01SIsbbmBhhrrRBj7VPsxBi1YuLIXJdpGpXrizD+3KKf4fJ2BrCNiM5h5j+vcdyqAlyZXgTgi8wsy2FHMvNdw97wVxLRdcz8Y+skRGcBOAsAjjzyyBqXtfdJG2tr0aJDtvEOOK+vSu007eprbE/V2jXXV7U299WLFnWFe/qLGAFg47Yb8awdP2yWErJ44gA8RBnr1YiHDCwDPDkeollrlywuO2SujulLAz4MBnDYWpZZDkFLLOMd8CFUz12mUpk2y7pq7ZzbAAfRZtlXtS5TrFpHLZuiubY0DYDrRADXAPgWgBcCOAlFu8j/Mbzt+yj6tL8AwLUAPkJEZ9Q47mYADxHbRwC4q2LfFwH4vLyBme8a/nsbgE0weWy53/nMfAozn7Jhw4Yal7Xn6ahP/dlKX0JUQ736visrn8yLKSEzppCqdVRUVH1FYx21nCKu/tlbNY3K9WsB3A/gycN2kSNdS0QXA/gGgJcy82uJ6MsAvgPg9Sgq3i5dDeCRRHQ0gDtRGOiX6J2I6FgABwD4T3HbAQB2MfMCER0M4IkA/lTPjRIaVkpsVlpXraG2q6P2rAYxDgTEqlJ7msvIY/kWLVrbrk6MgVVriYR4q9b93Pju29geDEwue8Dm9+R9dYdwMb5x+4149dZvYD0voErBKSEu9jpNxuU7IrOanqUg8RLAqeK007T4vTDkrmVHySwx7i9Oq9nrUYV79LeQsXzFvtXs9agqPXoM5G0yvtXIMyA12OsxEz2qjqc9ua+Y2zIfx3k6Zq1HVerxsVQXx8x8jHOKxei9UUV7FNWnOWzOzOcpJ2PWeoSWLO6flnHY4jGZ8jiKb1SlHv5OlLDFYQ9EWgWJqraucJPeJgZkbJ94F9ZVao2LaC1VYoiuUrs5bFXh9iEf8u81cM91sdWjv2uEQqKiZkvTqFy/AMDFylgDAJg5B3Axior2aPufABznOygz9wG8DsDlAP4bwD8y8w1EdA4RPVvs+mIA/6DO/ygA3yWiHwK4CgVzXbUQcq9WSNXaZax9mmY3xbDED7XdpBNj6CJGuR3QXGZkQBdVZqwBbNx+I9649XLsxwuVb645aqSEVF5IAFudmVVoTtUVOarUNoddHw/R3PVU00OasNUBRXkLLQmYq9ESDkA8LA47BC3RcwPOayEeDczxUkbxSYVw2NZ5HMbaPzmcw779rLfWP35U1DTEjp+9VNOoXK8F8CDH+IOH+4y0A4u1ELeY+TIAl6nb3qW231My7/+gwFWiovZYvfqeKzHH1U8lBvCVfX6lWbb1Hqw8IPPamtvglbNJV8cmnRl1S/QQhZjnqc5doZJsCFsdFbW3a2/GP6o0DXP97wDeSERXMfM35QARPRnAG1Cw1yOdBOD2KZw3qqGO/sRHjOg9+dWyRjosXMSBfPhwENdCxZB9i22uHrOi9lxzG+Ago7k0QmvcUXvGtm4mo6vWAxMlGWEWG7ffiPX5bri0nTr41MGnO/epVNmiRuk4SGyrWD6kifF7cErm75mmi/cBl8b0ye3UQjwkHlLVtRGowEP6YzxEfothdWJskfF4kp0cC8xEjenOjLk5ThVdHUuj+GRUXypQkgxGl0ZOzeeahYsItIQTNhKAOGXzOZ3CKHnIqjalDGOpeKK2NcYh8BHLWCuzXI6LjP6PyjGvqpbW15CFeHjwEKMSrff1Va3lsTxVahqQ+WuVrI2+/TV/aN8YFbWU2ssr1FWahrl+AwqD/XUiugnALSju6mNQ4B9bAZwNAEQ0B+BxKNCQqBXU0Z/4iLGt2WqXfObZObdJMxmHsfbObRLT16QTYwAOYqki03rj9hvxh1suc3KW85RNDwfRaSGucqJOD2mChyjUxIV4NMFDLKTDmls51ZsWEoSWNMJDdHpI/blQ+4Ykj5QZ69pTHcZ6VuVjq51zXcbaIysKNRrrqFnS7D91l12NzTUz30JEJwJ4O4BnAnjacOh2AH8O4EPMvGW47zyAX216zqjlVQhbrRVinu3zTj43pJviNOfqqnXQXF21Ftq4/Qaccc+3sGHYgdGVDLI9mcOnD9y4PDiIjt4LkDbTe5tCYvusuQ2CVoLMs1YDxKPJqrtGxrvJ1CZzQ9hqPTfmVkdFrWpNpUMjM28F8JbhT9SM62HnfdTEQTQCIra9OIhzrtrXh3wMAva1Uj4cY56qtYGH+HCQvpniIRMtSCEcOh0E/RwsxwcDkRbC5lieL1aMN267AW/8xdecfPVI22kOL3roa80bOaBaHlq1lu5DJolgmHktT63TQ6SByBRKUpYeIhGPVlL8LQBwRs3wEJX4YXVqtFI9yueywD/G+4ptlR5CCgexujxW4SEaB0mh0kO4cpzTsmQRsW+invOpwEkSM1mE9WejhA0fS3Jbe0Vtll0IiMdnNjHeuhJtDnr21aeV4x48RCMg8m+NXOMfjn2H+snr41tv1MqIEJnrMs1y+/OoZZDLWPvkMtbe83qi95zndRjr8LkBX2Nbpj3AtGqTXtH+u0xnbP23WsZ6njJ8esNp5o0hxlrLZ6wdeIjVTEbhIa6M65VKD7G7KdbHQ3w4iCs9xIeWuKrUeqwJHmKZ5ZAKtyd6z6kGbPWSGWvvZL09eQJImVmuUsi+UVHLpmiuLU3FXBNRAuCpAB4O4ECULCdh5vdN41xRzfSw8z468dwQ86y1cnhIg7khfLRSEFut54rq7sZtN+CQIQri0gCE8zY8FZvWNUBBdNU6RAEt0C01wEM4IGpPKySmb5pzm+AgIRF/1nm1EQ8xwGpfy4i75Ktah8xdLvmq1iGHaoCH6Kp1mWLVOmqlRTFex1Jjc01EvwLgEgBHofqlkAFEc73CevhHPmr8gay25i7EI68eA9wNZKwxfayA9BBf1VqiBSE4SLG/SCpQmEZZgxhpMKg/3p/K8A9j35IEkERcPwkc5O5/9fqLecpw3oanYdO+yljnPLlhDqlaE1mNZqxmMvLFNxk3kOEsMVARTlNA4SHy2JyRyhAf/355y04pceEhUqV4SN88lmwYI3+/IsFEHgs2SiKTOMR5OSlJC5F4iEJA5Ct2gXiYx7LSQvrm/uP/s4GGcaJevHWzGXLsq3ARa0GAMNOWKS/BQwy0xPXgJ55ewSygmYyvam28vrFCPPRnbkcV2/oMUmK0b3vTm63boqKWVTEtpFTTqFz/JYD9UTSTuYqZ75/CMaOWWY3wEE9nRud5p5ge4loQaM91R++5ZDeICUFLyhNARtq47QacsWUTNvS3g0FIK161chR2Zku2Hhcd+JvYtP7RzVZfOS86IC1EyUoLCVgAaaeHaLSk+lj6vE3wEOtYAekhPuTDuW/qHjf2VXeFVaV2oSUeHMRZtNXVb595dmnyh1kzNcJDJkc8muAhUVGzpMhc25qGuX4cgPcy8yVTOFbUEunhHzFxECvaySFf1dolX9U6ZO5yRe9pkx7CVlvRewFs9an3XY83/vwywVeXXzMD+PChzzIr1dpY5w1e7XTV2iXtgALwEJutnpx5cHHY3rkrhYc0IGlWKi2k0TU3Md5ay/Ru7q1aOyfrRYz1p8aqdVTU6tY0zPVWAO5uFlErLpkAYC0e1ObZYaZ9ixgt4y2OlfhwED1XkgTa8AYYbx8OUiAdYtvYNzdukNhGMTdXE6DGHOkhubl9xpZNtRYubsnWF5Vql2S1NaSi7SsXhmRcE9lpIeJarHQQiYfobd2YJktAww8QnNppIXLfPCPj2wXWaSE6SUQngFhJI8ZvOR5L7bmpSgshlQiyOJbUTwsZbRvH1ekgFi4iLll8eMrTEsRDNptRCIhhkBMABlpS9jirwEVIoSQWl11yqDpjTeUKMWmChyhZfQUcr8Gxoh0184qVa0vTMNfnA3gxEX2CuUk0QdRS6REf+ljtfZtUqe2ujvXnWqa9QQJIyFwrAaQB4qFxEafyHBt/eT3O/MVV2NDbjq3Z+loLF+cpw0UHPcm8cbkWkzTAQ3RaSBAeok17wAJIXVkOWQBpJ4A0SA+xUj3qzw1CPKxFi2oH113nWbToqlr7Fkc6K94hxnop1eRp1AgPMTfrzL31rbFqHTU7iliIrWmY6/8C8GwA/0lE5wP4GUr6RzHzlVM4V1SgtLH2Va1dahK956tau8+7MtF7VsZ1ANPt68y48ZfX4+w7v7pYqT7UYawHIBAYW7P1uOjgJ2HTvp6qtdQ0q9YulVWta8pmq0OMtza8kxvvEMTDjtprYNoDUAuLww7BQ9R5QuYGmXTf3EY4yORTG6kJHqIU1A03lquiVoOiubY0DXP9NfH/X4V9N9PwtgaUYNSk0uyx0ZvBWrmu5rrSQ3x4iOPYPpNuGWCJh1goieajUalkwOY30bphjBo3Ez9yoziVqGYyuoGMxj+kCaQB48xflCMgOUzfMk8ZznvQM7Fpv+OHF1XCVstz6fFprQrTx3Fw2Zax9lWtdZJIBf4BlOAh8le30BEyHh95auIh8lfIU7LSQciRHiJPXKSByLlQ6SEmSiJ/3Tw1P3jquRrxUOtfkSocRJY1NC6iDbGBkhBgIB4aF1F/QmmYOTGTR1z7FuMuHMRTAV8mc+28DG8zGXWs3DXmqXiXXMctf/wmx8VFRS2zOFauyzQNc33mFI4RtQQ65v1m1dr5BAgw1lo+Yx22AFIhHiFz9QeJkPQQtW9QAoiVHuIvNx3S21Y59otsPTb0C1TkokM2jo21ll60uFx4iC+mzzlXL2IMSR5RDWJCKs26iU0rYK5uLhNwzRaWElIt9lWpHdVj375OxEPP9ZnlkH090XouzYSx1gox1r7zRmMdFbXHqLG5Zua/nsaFRC2trDjZkBf9Jp0YA9qle+d6qtbm3JKqtBx3RO9pY63NswstKYvaO/Xe63DmXVdiQ28b7s32xdbWvpUeY2u2Hi875nXFsbRZdpnnpTTWIYiHr2rtkjbeQXiIO7bPJct4B5lnfR21p9ocdgPEo0knxhDTanPYk0fthSAey2WsvQq4EJ9ZduIhsRIYtZoUH6+WYvvzPVTHvfdjEcSZAZ1673U4+2dfwRwXZfUN/QdwcP8B/Pfcg3D0wlYDDZmnDBcesnGFrnRCNcBOQkxsVNTerli1jppFESIWUqapmWsiOh7A7wA4GsXnmNsB/Asz3zCtc0TV03HvLXCQxSqxL2rPEfvkRT5yx74BVWu70uw7lmBnPThIKfKh4vVGBalkwCpOz2arKzszara6n+PMn1+5aKwXbwdw4GAX/vyIZ+HMu6/Cht42bG2tx4WHnopNB5xQ7MQMlheZw7wO5uqq8DJVsa0qtYutBYzKtK+5jO7qyMm4U+OoKj2O4lNcdmKy1XmLjNg+YPztRCmXbXV5HB1neKnD8YLTltdsLtTN03FzosXzDp8vVtdG3U0xGe87qkqn+XhMstU6ao/TMYtdlviho/XMKL7xsX2Z1qyi+Iz1Cr7qeClrXf54ZvIUuZt8RnM8VbyGwQD+3XNdVey4aDFq1Su2P7c0FXNNRB8H8FrYL3PnEtEnmfmN0zhPVHO5zPFSzm2SNOIy1v7zullqF5ftw0NcXDb1c2zobsMh3XK2ekNvGzYdeCI2HXhicYNcqWYtWlTHnkU8JMBY23PdHDaHxPYpHlx3ZnSpCR7SJMXDG5/nmjtFPKRZAkj9uaGLGIOOFaIpRu+5VIel9unmd8aqddTsKlaubTXot1WIiM4G8DoAXwLw/6Bohb7/8P+XAHgdEUVzvUx69DtUpnWsiiy72nkPL/n5v+Ez13+ycp+trf2W8Yr2XLlaoHvnNunM2ABpceVj+zTVDonLpCl2F182zZJZiMY6aqbFnp+9VNOoXL8KwNeY+fnq9u8AeAERXQbgLADnTeFcUTW0iElYFU+1Ywgeor/mdOAioekhsrrsR0mqM69r4SAVx1r8Cn5YcfV2ZhzwYmrGaXf/AC+/4xvY0N2G7dlagBn7D3bj3w44Htfv8xC8YvM3DTRknlq48MGnjVGBAY+rt1wSrSerkRoXkVLoyFTlq0y7xshREdZV65KujrJSXyAg4+OYMX1632rkg9OSro118RAreo9sHMRCPgRK4ojaK+2uKHAQZydGUuMC+SjFP4QsE2tE7dn7+mL6qGJMS0cAGvI9lKf5UHdG73kuI2CuEw/Zi41IVNSepmmY64cD+AvH+FcAfGQK54nyyKpau9QED/FlWgfMDUE8XMbaOzcAB/GdV26fuuUavOknl2IuLxzT/v1dyAH8/WG/iYse8mRQnuOBdC3OvOub2NDdhq2t/XDhg0/DVQed6L+OkLSQpdRSGWslX9KIq0ptoSS6uYzrvLqZTJP0kAYJII3wkJC4PM/coO8zp3neEC2XsfYoxBBPgof89zmxah01+4rrBmxNw1zfj2IRY5UeBsDf1zmqkY5/28eAVvV4I6yxydwGTzqrm2TQ3MkvWndmdOmVP/36orFenA/g9PuuxUUPeTIA4KqDTlw000sWp7caX9wCWOpZVZPEk6DovSnObQIDzkwk3nKpEZc9tauIipptxce6pWl1aHwdEX2bmb8kB4joOQBeA+ALUzhPlEeys5uzE2PIynbP15ou5MPeV1eAq48VgoMsHpvG+xrdFAcwGp9YCSByrkj/GI3JRXKjtJBW3sPv3vkdbOiWf27c0N0GJOZiPI2WgHlcNWWVlhC6iHG5fKqjah2cHiLnapOqK96JSg8RaSKcJsbjlBMTLeGMjMc4JzRGLTIyHnd5Chs1UUkko8densFGRxwdEjkZ4yR2OgjZOEgFAiJRkcVtCBGsNJHFBBBPaocTAfFVqR3H9ppyfWzX/ktp8EPSQ5rs68NFANzwwVi1jlodikiTrWmY6z8BcCqAfyaiWwHchOKl41EAHgHgjuE+UUuk498W0IlRqVFzmQadGJugJaXGuu55NR4SMneQA8x40j034FW3X44Hzf8SC0mGTm7zKVvb+6m5kyMeTmO9nArqxOiZG4CW6AQQJ1qi99UdEh1t222ko/5cG0PR11U51b7mBgkgjTCNBs1lmjSICTLWS6mQp9kUK9rRWEetajFWDlmcYU2jQ+NdRPRYAG9HkXP91OHQ7ShY6w8y831NzxM1maYRA1V5rKC5k584hI/WaoKW6Gs+9oHN+IPbLsMJ23+G29Yeirc9+mXYr7cTb/7xlw00ZD5p4bMPOX3yE69CxMOqWofMbYRWrFBaSJPEjyY4SEAbc2vuNA3wcrHVnutYLjWqzEXfERW112kqOddD8/xHw5+oZdSJb/6YUSS03ntWCg8RxtS/r9ouSfEYyVe11nPlG3uBfJhzze2x0Xvy5u/hFbddgUMW7se97fW4u7MfTnzgDtzX2gcfOea5uPyQk5HT8J5PgFfcfgU2LGzD1s5+uOAhT8FVG04aH1fiH4ATASGNeLjSQcqOtVwKqTwH4CL2vu6qrlExScyFP5z9AAAgAElEQVS0kDzTuIjaX6ITWRmWUY2HJP3xdVl4SKLTQshEPNQ1mdiJRj7s6zKvcbxdmtohz0v197XGUT3mrTw3wENqj01b00oP8bzGlh3ruo/EqnXU6lLEQmzF9ud7kFzG2jt3mnhICGrhMNb+uQ0SQPRccR2n//wHeMvNX1qsRm/obsfB3e34jwMfhQ8+6oWYT9rG3KsOfgyuOvgx4xtcv7+nQUyQVuqruIAqdRMO22esnYhHpiL+AubaWEYAHuKdWznVe43OanENdrrueZ3G2qNpVsdXTMu1+Dsakqg9RfGxbCn4O1UiOnKSn0kujoieTkQ3E9GtRPT2kvEziGgrEV0z/HmlGHsZEd0y/HnZJOefdZ345vrRe43wkKka78nnBkXv6fM6uilqveqWr1kJIATgETt/jt1Zxz1Zm/YmbPVq5NhC8BBfhTtEDsPr04rhISvVEGaaEXgBx5qZ6L0mavK6WWNurFpHrTYRisd21c/eqkneGm7HZJ9TgkhDIkpR5Gc/BcBmAFcT0aXMfKPa9QvM/Do190AA7wZwyvBavzec+8sJrnsmddIbFA7iW43e4KtKJx6iDKDLTHuxFEc12Zs8ohuoiF+qSAep3neUFkKcY+Pd1+GQhfKW5Ycs3A8QGdVIytlkaMUfhXKYSIdGOBgWAlK5r08rxWk7q6kB5llXS+ukhzjmGs1kkpK0kIrnAyew97WaupTjIRbSIZrHVB2rcq4PFyHH7+9DPFx4SAjysYwJICv1Hu28rAZ4SNncH348GuuoVSjm1VkIWmJNYq5fjuV5rXs8gFuZ+TYAIKJ/APAcANpcl+lpAK4YLaQkoisAPB3A55foWmdLE1RMKucuW5XaPHGjKnUQllLse/K9t+D3f/Q1HPPAnehRghbbF7+ls7/7PCEvMJ4PKUGaRWOt1Kgq7UkPcZ7XQivcaSLuue5xY8xKC2mCh6htV6ShD/Fw4SGhLLVLe7qx9s2NXiMqaq9WsLlm5ouW4DrKdDiKGL+RNgN4Qsl+zyeiJwH4EYA3MfMdFXMPX6oLXW6d9Ib60XtBFW2fGhjCJlhKI/NsRe+Z48ds34xX/ehrOOW+W3H33P7438e/EMzAm2/6kpUAcsHDnjLxNYcZ7z3gnTkIDzE3g9JDGsy1Y/rqnzaIh7bmuredc61Iw5C56oaVmhugmTXWTdjqWLWO2sMUP0zamuUFjWWvb/pP+C8APs/MC0T0agB/DeC0mnOLkxCdBeAsADjyyInQ8GVX2nUMhphnn1lugJa4zLSFdOi5jvg8FzpSNm4gHANeNDKH77wHr7z5cpx297XY1lqLTx73LFx6xK+hlxRPiTxJ8MpbL8ch8/djy9z++MzDnoorD3usuGY2jRwD8mHHicBS1Jg2zxY+YvwCNV616gJXvkNNi2v1GGu7ulq/emxXRMV9bqEknmOpZjHG/UMayyD1WHPjHnYDGC6bOvol1HnVsQwcRD9ZzMO4cZD6xjzITPsqzyvFaU9RzYoQU72UqKjZU5NFwERPB3AeineyzzDzB9V4B8DnADwOwL0A/icz305ETwHwQQBtAF0Ab2XmKye/kulqKuaaiH4LwNsA/CqAA1Dy8skcnO66GcBDxPYRAO5Sx7xXbP4VgA+JuRvV3P/b3pnHWVKVd//73NvdM8O+zKCA7IvgDo5IMBGQPUZJDAbcIeD26qtxiUZNXIjxdd8iUVEJLiSoaBR92ZcJvjEgmwmrYQQEBJ1hmGEYmJm+y/P+UXW7q07VPVWnq27f293P9/PpT3fdc56qc6vr3vrd5/7Oc1bkHURVzwbOBli+fPnIvw0e/CbPJMYKb+pBwrpoX1Wy1BUWl8naNLJ9tt/8GK+760pecv91tBpNvrXPi/juni/kiebiVL8rdz6IK3dOiGlfJZKMWKqQ0R8WsyRiQoR1hiKx6D3ukLLUrj2kSPB7jlNF8A5MWBdgwro45KYvW9bamNvM9ANkybl1pwNrVXVfETmFSOedDDwMvCRea+UZwKWMkEOhsrgWkWOB/wvcBXwfeBPwL0SfQnoe6Z/MYNfXA/uJyF7Ab4FTgFc6x95ZVR+KN18K3BH/fSnwMRHZPt4+FnjfDMYwUniFdShBwtsfG5QdL8hap/sWxHqE6dH338Qb7ryEnTauY9WS7fjmPi/iSZse5S/u+RkT3Tb/9ymH8M19juKRxdtk953JpLtj9gntguc3LHvIbH1srOKtdgjyVhdkrQcWW6Non7VVDesUrcM67ixRJetsGWtjQaD4y8/6KTO37kTgw/HfFwBfEhFR1ZsTfW4DFovIIlXdPNPB1EkdmesPED2x5wHbEonrc1T1KhF5KnAt5SYhplDVtoi8lUgoN+N93iYiZwI3qOqFwNtE5KVAG3gEODWOfURE/p5IoAOcaatELhyOfuAm3vPfP2BxJ/JMP3njOt5z6w8R4OonP5Ov738cv91i2XAHOUoMS/TUKMQNYy5iWWtjXuDX1ktF5IbE9tmxYwDKza2b6hPrwkeBHYky1z3+HLh5VIQ11COuDwY+oqotkalcYhNAVX8lIl8hWhr9+6E7VtWLgIucxz6Y+Pt99MlIq+o5wDmhxxxVlp/+WX/pPR8VMs0uvix2UZbWG+vaQQoy3qhjVU2stviGOy6ZEtZT7cCaRVvz4eWvydai7iayiKopsSnqTHzTtD86lcXu9o403ZY6kuu9JmEbyM1SlxOfs5khq1ar2BNcZGNoeNoKy8klvfBOW6ZMX86+4v/N9DUyfVyfL1sFJK560/s/SzI2uapjzph7++q1TXXJeb59z+xUg+Yfx0eolzogNruvufdBK6jCT07XG77xzvoGYxijy8OqurxPW5n5cd4+IvJ0IqvIsTMb3mCoshRBkl5h4Mfj3zsk2n4NHFDTcQyG91VlmaV7BxJb4KVOLbWuXZ60cV3ufrbf/FiusO573EI7TP8nUeRhn4sMTFhnjtNfWBfHVjhuwKqG2VjnQ1OlShwh56r8ceqkzuMuRGFtGPOJCovIFM6tS/YRkTEih0SvzPJTgH8DXquqv67+TOqjDnH9G2BvgDgl/xvgBYn25xKfCGNmLD/9szMPXihv7Koc9rvb+caKL/RNmq1asl2fFmOoVBLtFWIrrOo4tDHXObnQGBqWtTbmFb2FZPJ+/EzNrRORCaK5dRc6fS4EXhf/fRJwlaqqiGxHNN/vfar6HzU+m1qowxZyNZHhvGfP+DbwfhHZksge8mqi2aDGDHj+az4btrRlgkp2gcDJdN7VyNy+vjJ9OdaStP3D3ZeyfPVdvP6OS3ja2vt5YMsd+cEeh/LiB25MWUM2Ncc5+4Dj+5bmm/6qX6bGkRIjjl0kCklaQJj+qOpYR1zys14zVD7dIX1+quk7r8KsZcjEvcyKkP33VVi2L8+moYk210qS/J+KYzVJlsjLVAvJe50m9zUdkvfcvWevQka4NiFeaj/5V/CofBio9j5a2zAMY2SZ6Wuk5Ny6bwDfFpGVRInaU+LwtwL7An8nIn8XP3asqq6a+TOpjzrE9aeAFSKyWFU3AWcSmc1fAXSIfM9zvlKH0Z9ZK9PnxD579d28/o5Lec6au/n9ku34xHP+nIt3W06n0eTWHffkjXdMVws5+4DjueIpB0/vy1daz6Xgg0alkodVmAMrM1ZiQMK6iCo2jcJxhDBLNhyXURG185Xrvm1Za2MeoVT6EFlibt0m4OU5cR8FPjrzIw+WyuJaVe8D7ktst4G3xD9GBQ47+TMwsYDvdH2E6AFr7+f1t1/C81f9D2sWbc3nnvWn/GSP59NqTOf4r3zKwVyZENOV/OEhWKaqNCPjta1itajwQaNKrDE71P1eYMLaMBYGo7xCowE0J0dcrdUpWr0TBGGf9Q9y+u2X8cKHbmPdxBac9YwX88O9DmPz2IRjS3GsJEo2C+pUAEn2TY2imxOc2lH/U5D3fH12EWOawuypr71ocqHv31mQtU61Cxn7h7dCjsjUxZJZTbEE/aqF5DPi7xuGYcwLhJq/kZ0nmLgeUQ47+TPDHkIxdb6ePC/O3dev4vQ7LuPoB/6Lx8YX87UDj+N7+/whG8ejVRWHlmm295OBMCeEdVGsy6hk6Y2h8fPvvmvYQzCMwTAqKw2PECauR5A/+rNPzzs7yEwE8C4b1nDanZdz3G9uYnNznG8+9SjO3++FPDa+xUCPO8XMV52y1dnmO8Oq2jG/3hYMw5gHWOY6i4nrEaWsHWS2btSDnOR0zH038aZbL56afHjefoezz2O/50/u/QUdafC9/f6I8/Y/knWLtsoZWP+MY6biRyY2rwIIU7FBpdrmX1nr0aTOa9aTTQ693jP2kMwONbdvnVRZWKrWfRsZfvZv7x72EAxjMFSc0DhfMXE9YvzRn3162EOYVY657ybee9MFLEksVf7O//4xHYQf730o337qUTy8ZNv84FksNejfV327MjyMirCuYg8ZEIMUvyasq2HC2pjflKpnveCoLK5FZF9VXVnHYIww5kPW+k23XjwlrHsI8Mjirfncc1424/0OaxVLwxh57Po2DMMYKHVkrm8XkX8CzlRVW4mxAof/8SfTJYQDJm4V4a8R7O+b2gyt4+tp37K1se9S5Us3rQ/atyuIiz8QeDKXoyg+5oLXdhTPG9R67qp80KzTtuFWxwk6rqd74RgrzEMY2etjgPz7Re8Z9hAMY+BYQipLHZVWPwacDvxaRP46XsLSqMqQBEGtWeo++1rcnuRVv7qa71368VqWKq/1hW1vEvOL+S6sQ49rwtowjLqZ+fLn85bK4lpVPwzsB/yASGj/j4i8sup+FxqH//Eny3cOyFpXKmuW6Vtt9bmJTouXr/wZ37v0//Dm2y7i9u135+wDj2NjczzVb2NznK8844Swnc9nLGttxGTFcshKoxUObMI6GMtaGwsCjVZP7vezUKllQqOq/g44Q0Q+D3wa+I6IvAN4t6r+ex3HmM+86JiP0/S0Z1ay86SYvJYOSAnkvBrAqYcyNYO1b9/cih1x97Fumz++73ped+eV7LTpUW5ctg8feNpruWXHvQB4cMsdeNNt09VCvvKME7h894PJJWci2jAmkc1Ghr8Mw1rlr9KbZo1ia2S+jixYAMkf6+nrW5SmKNYVxM7m4KwlAf+U+XDzzXkNXnX538z+OAxjWCzgDHU/aq0Woqq3AseLyDFEIvsqEfkp8B5V/VWdx1ooZIV1hZ2FLGRRtBhHieM0tMsx99/EX955Obs88Qi37LAHH11+CjfttG+q+xW7H8wVsZgeVrWFoWHCesbMN2FdtN8QgRskrAsYmLA2DMOYpwyqFN9VwGnA54A/AU4Qka8DH1LV1QM65pzkRcd8vHznAiHmn7RY58p1/nbRLkf+9r857c7L2XPDKn617a68+w/+kmufdEDwRMz0jkdDeS+4DwBGf6qIyQqCt16xPEvWEhfLWhvG/MA+U2eooxRfE3g68Fxgefz7WcAiIhm2HrgZeBXwChF5t6p+o+pxjRFElRf87nbOuPMy9l3/EPds/STef8hruWaXZ4yMMDYMwzAMoz7sG6ssdWSuNwATREK6DdwCfBO4DvgFcIeqqohsD3wUOFtEdlDVT9Vw7DnN0Yd/rO+M0l52dEqSFonTRHPGStKA5EdLFUn7pZOD6MXGXyur68NuSupTqgpIV3ne6rs441eXcuC6+3lgyx058+BXcOVuz6ErjSi+t1pi8jXoy4DXWPIvyD4R+BlgJnaZyvuB0ZjkWOcqfwFvzr39lI4Iyer6sql5+0n8T/MywlOv4wJrhdsuSVtHUZa647GPuNYS5/llbopdT1tAxrvYDhPw/x6RDHeZ95ErrvnA4AdiGKOGiesMdYjrC4FriYT0jaq6Ka+Tqq4F3iIiLeDtwIIW10cf/rH6dhZUas8RrSHCs5kVvM9eczdn3Hkpz37kHn63ZDs+8eyTuGS359JpNFPHCrWWlG3L21cqdoC+5LqEdTCjIKwDGZo/ui5hXRRbwWrhFdZF+IR14XH7C+tCKtlh5t6N2IS1YfRBmR8Wr5qpLK5V9eTAkOuAt1U97oKiijAbkLg8cO19nP6rS3new3exZtHWfO4Zf8pP9ziEVnNQNn5j3jIHxVYlhrR66KhMRA1hVLLWhmHkI6jZQnIYhhK6BDhlCMcdGY499ExnJUY3I5ywbeSVuEv2dcWzCMK0pYNOflsUK0iiXZsJ30bClnLUgzdzxspL2WnTOh5ZtDVrJrbmqY89yLqJLTnrgBfz4z0OZdPEongnidietaQhKVHgjtlrB3H7Zp6vZ18uBR9SQlax9MZWmnhaIXZIVKmIEcX6vnrI9u+7uyqLr6imri1/X2e/3fT/RbqkrzXXHuL2Te2rvxVD3Cx1pVi3b/8MuBub+TAUUj2k6CY8omI6XaI0237ZtR+craEYhjEHmHVxHdtDvjfbxx1ZcoR1WfKEdflY57jN/NijHryZd9/+QxZ3WwAs3fwYO25+jKue/Ew++ayXs3FsUd/YMmMOWj1yLgjrAqqI41EU1oVUSWhUqabhUKmaRshxXHEY5C0uL1rrjC0s+eeNDehbxIgK6xRDKn9pGCONZa4z2Hf4s8yxh5458+Aq9pAZxr7xrounhPXUroCnrbufjWOLCuNdER/CsOo4V6JSHfLaRjFrzJbgLaSSiJ95cCWf9tBih+SXnqc3YMtaGwueefraroKJ61nkuIM+5P960anikV5NMVsBpF9fiK0Yvb+Tdo/ecRI3WG1kt7dsbeSU+65h6eb1uc9lp03roq/DG+mvjTVRTUQbpF502nAqjWQsH4nn6y5ZWZClTon4kIy20z+k/ndo/5AFgULHMeO+RQyy9nLykix6cw7Zd5GVRNNt6Qsg3Td3omLC4pG8TqXjXNNKuppIN2EPyVhJNGstSY4j8RorsoNIu78FpNAO4rOPFEyA9FpAAidPBnk467yxW8lQwwjDJjTmYuJ6VMgIr4A3+RxhXRa37xgdTrzvP3nVvVezbXsjGxvjLHEy1wCrFm+XFcdV7CEhY/YJ61DqtGnUJKwzDOt+P8is9KBEe1HfSrEhgs/ZV0C22CesC2M9wrowtsiX7YsN9VaH7MvHkIX1Jb+s8E2kYcwTbEJjFhPXs8RxB30o/UCA5SG/bnXJWFfw9rmBiHY56vf/xWn3Xs6TN63j+h3242v7HM/uG1elPNcAmxrjfG3/48oPghESwLN03CrMxTFXE+LDeWOu09KSnBhcGFs0EXFgsRUEcJ1l++ZJlsuEtWEY/TBxPUvIZHt6o2giorMtSXtIUzIVQJJoo+F8hZyIbaTtIdoUpN3leWvv4vR7L2Pfx3/HXVvtzGee9WfctP2+aLPByq13ga5yxt2XsdOmdaxavB1f3/c4rtzl4NSNXseyFUF62123PnZB1jr5VXthhjuTxU78HVpbO2UPKTiOb98hfR0q21JmiUrZ40z/hHWiKNPs23emTfvaQYpjHXtIF5L/jKRelK6iY27fxHbHvW7TFo9kW6Ojqf+5dIFkrMfiUZi1Tgy6MEudae/2byvKWs8Fe4h3gSezihiGF8tcZxhpcS0ixwNfAJrA11X14077O4EziFaGXA38par+Jm7rEK0WCXCfqr501gbucPzTAxYXqGAP0YajRAtsGvs/9lvOuOdSDn70bh5cvD0fPfAvWLHsmag00Ob0vq568kFcuevB/Y875hfP/jH3F9aFeIR14XEreJpHxQ89ksI6tG+ASA8R7UWrC/pjne1MBRBPbI6wTrf3D24Uldprlz/xGeHdCUgX+4R1EXPRHjITO8ht/zDz4xnGvEJNXOcwsuJaRJrAWcAxwAPA9SJyoarenuh2M7BcVZ8QkTcDnwR6i9psVNXnzOqgczhh//fCeOI0VymfV9LikRubELE7b3qE0+67giMfvoV1Y1vwpb1fzE92PYR2I/9ycI8b4q0uylr7x+xuz9yXXWUVyyriuVKZvrmYMBvkBMi6jltjbJXKG3VW/AgS3hW81YVZax9z0B5iWWvDKEAxcZ3DyIpr4BBgpareDSAi5wMnAlPiWlWvTvS/Fnj1rI6wJNJKWEKSb9YZi4MjRF17SDIL5mSpIztIokNToAtHrrmF0x68kmWTj7JmfBvuXbyUgzbcS0uafGfXI/j+Li/g8cVLov0nF31JZbqmj6Vj6coi3aakbrg6lq4IkhRMXScrnclau1dj0g5TsJiMb7vQ4uGpn13NWuKP9e7LbaOA2dIAriAO6Rtg+SjqW1QBRJ0KIKnYri9WM3aQ1OIyriBMVQNJf/OSsYu0+9tDGo51RDpO9ZC2s026LTUkJ0vtXVym7fZ1nqAvi+32zfjB3XaPPcSlxgx4FSRHXF981yeHMBLDGGHmwAfl2WaUxfWuwP2J7QeA53v6nw5cnNheLCI3EFlGPq6qP8oLEpE3AG8A2H333SsN2OWE/d/rHqx0bJVJjD07yJFrbuGvfvMTFms0GXFZaz1LW+u5aZu9+OR+L+eRia2zx83YNMofOGMPCake4lyJYRVP/NshscOyh8yJSYwhwtoh1EvtPW6V2CoWj4LtdJubWfa3p9pcQeuIZ9c+ko71C2tfhjtIWBfgFdZFjIiwznu/NmFtGEYZRllc5927c99lReTVwHLg8MTDu6vqgyKyN3CViNyiqr/O7FD1bOBsgOXLlw/uXTwjlgMsHq4AzMla53Hab6+cEtZThwGesumRKWHtCmIfbl/X8uGjKGvtPW4V8VzB4jEqvuw5+YXbbNlDao0N8TT7t/2xrlguH5vxZYfYQdwMd7uKWPZnrdNtFcTyCAlrwzDysVJ8WUZZXD8A7JbYfgrwoNtJRI4GPgAcrqqbe4+r6oPx77tFZAVwEJAR14PihD3f4Q7U2U4oRDc77FlMJuqfsGE0JX3jazahrRz4+P3s1Ho0d2zLJh9F2l10rJG64bqWD2020tsJxahNpzqIUy2k28S5MaZjk3QLstYpi0ehHcTj8S6wg2TrZ/ePLdyXp28la4mLr72KPqgwEbGMpaNve0F23GfpcLczAlhd24YTmqxakrGDqNcektyWTvoab3TS9hA6zvWftIe008dpdEhNTM7cwxLPp9Hqpm0qAZMYU5YystlzV0yn2t3jZLzVPntIQayLLwMecoOfgXi++L7PB8cYxoLAxHWGURbX1wP7ichewG+BU4BXJjuIyEHAV4HjVXVV4vHtgSdUdbOILAVeQDTZcVYIEtZFZIS1P3bp5KOc/tCVHLnuNjoIzRwVtHpi28LDunaQ7nj5MbtZ6m5IdrxC9RCvsC6KrVJ5xCesA4+74BaXCdFDRZaOAMEfZg9x7REF+0qQyTS71UM8Fg83o531VgfYQwKy1pn9FgnvJCHCuijWpcqy7YZhDAbFXps5jKy4VtW2iLwVuJQoP3OOqt4mImcCN6jqhcCngK2A78cTT3ol9w4EvioSVYkl8lzfnnug2cYV1jlZ6744fZPZsUXdFietuY6Xr/o5Apz3pD/ioYnteOsDl6SsIZsa45yz61HomLMvjwB2hXWmeogvtsCH7Wat032d7QB7SJGXukqst061+0Cd1pIQBpm19h22gniu4uku9FYPKLao9J431hGpjQoVPxqtgCy1O4mxU37QWeEdcLIy9pAKsS6WtTaMIaGWuc5hZMU1gKpeBFzkPPbBxN9H94n7OfDMwY4unxN2/d/p0nsD4IhHb+PUVStY1l7P6vFtuHarffmDDSvZqbWeFds9jXN2PopVE9uhTaHdGOO0B69iWetRVk9syzm7HsXVS5890PEZM2Sh2Tzt/diYA5iwNowCTFxnGGlxPWfpld7LXRAmWS6vAd2O095rc9K2jQa0Oxyx/nbevuoSFmt0jCe11nPi2pt4aGwb3rXHq7lty91hrAGqSFtZse3TuXrH7OeMZPZKx9OrOurYtNe6O5Zu645L6oWkTUlllbrj/X3ZbhY747VOZLUzEyCLyvgF+bLT2/hina7u5FJv+byi7LknttZVHisQuhJjaT90kS87z1vd7BPrZo+d3aX21evbZ1+ZcnqJEnm9LHXv/5rxYXdIeasbnelrvvca6rU32o4vu62pbeno1Gug0dL0cTuOL7ul6VUe29Pe66nXufRiu6lrWFqO5SOZ1XZ91q6VxM1ap1ZxdLLh7s3X59MutId4Mt5FsTZR0TCMWcDEdY2csOv/Lt85xA6S4NQ110wJ6yQNiIR1AeqIdnUtH2P9/RJJ4RztS7ztqbYAYe0SIqwzsQHCOhPrPlAwAdJ7nJDSe3NBWBcxSG+1L7ZoXwHHDbN4pLeDKoAEeKkzsS3XWhJgD/EJ6yJ8wjo0NiTrFXIcl5nYQR46a+bHM4yFgmWuM5i4ni2qZEziUnvj3TY7tdfndlnW53GfWDaMuhja6opVtFYVb3mFCTyN7Gfj8scNEN7Z2JmfrCDh7WI3XsOYv9iExlxMXNfECU96c/qBpJjO1LhupDMwbp1qkensTrMB7TaHPbGSM9Ze0zdpuXpsG6TdQcea0LsRjjdz7B5JO0gzvdrieGPqRtirFjJlDxlvpDJ7XWclRh2ftoBks9T+rHV20mOiLbP0Ov7tEHuIezIDYr0WkMCSf+nl9gL6FlAli52xeHgPlI31tfvsIUWl9lLtBbFuOT3XWpLq20n3zVuZsXetSWY1RdcOoqlrPGqP+jfa6bZGO339u/aQRlunYiVj/3D6trrOuLpT7Zkyfa1Oerudbk/aOjLCuu2k5TNZ7I6nLXA71Zb+h2vIhMgCJKeC08Wrv1Lb/g1j/qJhk5MXCCaua8ArrIvIEdZHPH4npz76c5Z1HmNtc0s2yAR7tNdyz/iOnL/d8/nTR29MWUM2yRjnLn1hJKxLouPpvkGl9lwx7LGDZGML9uV5CiHCOhMbIKyLYqtUHqlk8ZhFYV1bbJ3VQypYPKrYUkKOW1R6zxvrrsQYUD3ErRYSVOM646UOGLRPWBcRIqwdTFgbxghh305lMHE92/RZTbHHEXLWhG4AAB/7SURBVI/fydvXXjklnnfsPM4OPM6lWz6NLz75BLrS4DcTSzn1kZ9F1ULGtuHcpS9kxTZP9+63ij0kRHhnYgNqXGdiA1aAdAmpU11nbKWKH3NxrlWNQjyofJ67ryqxAXqwztgq9pAQ4e3ieq2DcLPWIVTxS9vXzoZhzCFMXFfk+O3PSD/gWeZcms30DUYknSVqNjlt3X9kJiwK8JxN99PtdIEuK7bYnxXbPC1znJTlY6wxtW8dG4OkPWS86Wy7FUES9pDxRmpp0+5YI10BZDxtD0lOaszYQZwMd3axmem/C1dx9NlB3DY30xxSP7tGa4mLNza0eoinbyWKii8k2wPtIT5BnGnLWW0x2Vd9fXO2Nfl3H/tH7nbCPpKxknSg49o2+lhAGh3ojifaWpqxh2TsIvHrpzHpWke6qVrz4thDknaRqK2R6NtBU6s6OnaRpBB3hXXb+XTgZrGT720F1UPUbU+t4lihekgJ3L1f8ug5lfZnGAsK81znYuK6TjzCOo8jNv4Pp264jmXdDaxubMU1S/ZlWXdDbt9lncf678ixg4Rkqd1qIUH2EFcsh9hDPMK6iBA7SGjsoKwlmX2FxIYI6zoJEdZFbQMS1hlKCOuyVKkekrV4VIkNWFzGsYcELS7jrsQYkuH2CesCvMK6iIrC2jCMGjBbSAYT1xXIZK09iFMC74hNd/H29f/OYuJ61d0NvPzxX/Zfsry59fTGWPl/mzp9Xa91CN1K1pIK9pAKV2klIToka8nQVmaswpDeW+u0kgQJ78wy5uVj3TJ9bjk9b+xkldJ7ji87RDwXZa19FNW89jHLN23LWhvGDDBxncHE9Qw5bqvXpbbF9VI3HetFIhsjzSanbrhuSlgn2cAEi6STnbC4zWHRDW6smb6xjY+ns0Zj6QohNNzqIJ5qIRNNZ9GXxlQm0F08pjvhZLyd6iEpe4ib4c5ManS3kwvR+PsGVQspylo3PW11VgvJ7Mv5irzK4jKevkGUyFr3repRaA9JDywpal0bRqnFZXL2k9c3s53UfF2mKolAJIC91UOSi7i009VCpJO+5httTVlAkv+XRtvp69pDWpqee5CxkkwPpDHZnaryE+27m/4WK/m2MNmGpmP/SH7z1nbsI8mT2+6kT0DGHuIR7U6GO5O19ghxLcpSD3gJdMMw8lAT1zmYuB4E7uqK8Rv5EZtXctqmG1jW3dBX92zNZj617TGc+ti1LOs8xurm1py7zWGs2PKpxcd17SEBWeruhFM9JCBLXeSt9sf69+Wjkj2kQHh7CagWUnRcr7AuOu6Q9EFhRRBv3/7COkORlcR33CJ7iEf/uZnlkNjMYjJV7CGZBWICMtxtN0sdkJbPxFbIUgdVHhluhvvSDd+svA/DWHAoZs/KwcT1DCjMWsdMiWl9nMdYxBa0GC9Y9WJ1c2tWbPFUVmwRi+nkvt1Se+PjlMUV2kHC2808TwQI74KstY+irLU3toJ4DvFhZ6givCuU3isaRxAVyuXNVsm7on0FUSG2WsUPZzvEHuIK78nyT6Ix6UyYnjV7SEHW2kNh1jqEnKy1CWvDMOrExPVMSN4kGpK+SYhAt8uRrbv5q8n/ZDFR27ZsLtztJsY4d4tDpm9YY2PpT4SdxE1hYjzd1mymxqUTTmWShMrT8aZj8Uhv61i6QkhSqUXVQxJDCpjU6IpjN1Zdy4enAog7IdJXA7so1lcRpHAhmsxy6lq+r0dMa6NAaIXUx65A3uRB3yIvqdiu5PRVZzvZP/9vALr+jLfPWpKxmuRYQKb6dqDrVABxK4KkrlPp39e1fEgbOqnPw9q3b6OlXrtIqlpKq5vq25zspqqJiFNNRBNPUFqdlHVEJtup14NMOnaR5CTtyRYpCrLWqdrUBRMgM2I6+X7kTni0BSwMY7iYLSSDietAjlvyGm/7ka27Oa11Mzvp46WShxr/rG5sxblbPp8VS/aLGtxJi67VxLdPx+KhEyETINMKsFMlSx1gD3GFdaXqISEWjwpZap+wLqRKlnpU7KI1Zri9mecAYe1SVHnEtYCkYgsmLfpi3ay0ONtNT5Y6k5V2tn2xTSeD7S4Q05gsny0Wt28Fi4cGZLi9wnoAXLrpvIHu3zDmPSauM5i4DiAjrBNZnCNbd/Pm1vVsw2SQ9lnV2IrXbf+KTDWRFG7bxLi/PYErrDMrM04EiHanTJ+befZRlLX2Hrco8zyo2BqFd1jszN+oBp21Lh3bLT+QWsvlBa2m6MRWqPgREuuK4xBriRsb4qV2hbVrB5FJz0BcYV2UtU6QEdYhZfvcm7ZlrQ1jxFCrc52DiesAXI+gqHBk+x7e3LmBbWgFJxQ30eSfFz03Wn43eXE2G+kbjuvpTraNj6ctHeNjqeycOMsxp+wSE82UGIkWj0lsT6QtIMk9FdlB3O2k6AutHpIUphk7SEH1kK7HHhJUTaTR/zzm7StlDymq+OGK6YBqIdllDqkNr/0jszJP+m/12j+ca8NXXaQrqR14K35M9Y9odPx2EPd/mNyXGyttv10o+RJ17R+NNmjq87D72ph+Rs2WYw+ZVO8k32biq5fGZDdVp96tHqJNV6gmxjjZTn9Ib3XQxBwPcf9Jif3SajuVRmY+AbKw5nVCTGsNQvuy1vnBMYZhJFDH8mUAJq5Lc+zEK1PbL+rey5tbYaJ6EmEjE2zNZlbLlvzz4uWsWLSvP96dtOhOavTGulnq8v/uTKm9CvaQTvl5l1lhXWUCZIUM92zZQ7zCuogBCmsvPmFdQCajXVS2LxXrbHuEdYYK1UNcS0cVe0ij5bZ77CGZmtbl7SHuBMds1tpzM3Qz2m61kJbzJJI4wrowa+1jwFlqE9aGYQwKE9cz4C3t63mJriy9xogCq2RL/nniYFYs2T/VJhlvtWevBdVCdLz/v9MV1q4v27cyoyusQyYxusI6pHpIxoddkLX29g0Qz0ViOajySJ3e6rmwQEyV6iFu3wBridvXJ3gzxw0Qyy4Ze0iFlRibHs3qkrGWBFQLyazi6LODuLjCOqhMX8EkRq89xKk0YvYQwxgdzBaSwcR1CY5pnow0mxzZuZe3d69jCd3SOmcTTT6/6DBWTOwz0DEaI0IFv/TQSu9Vocp7qr0fG0PCstaGUSM2oTGDiesCPiaHcgE3sU03ytiU1TAKrGcRXx5fztXNPaHTiSYtJrMzY2OpbI2Mj01fpL2Mdm/mfLINMl5rxsdSvshkuT2dSLe5pfcyC8gkMtWhWevORHJlRtJ9M4vNuO35f+dt+6qLBHurUz5cJ0vtZrHddvH0dYV2iC/bic2U5vX6J/o3BeN7z3QGnUkeugvGJL3YblbaXXmxK6lznSyn13vqU9dAJ+vL7qRWTEyUpYtfblP7ylmJsZMs09cGnegT24ZOsvReK31dNjrT13jPHtKdmO7rltpLfsvTbEE3fi317CG910/TXcVxsptd1TH+JqpnB5neTq/a2Jhsp0vxtToQb09NcOx9m9ZyfNmTrbTZvNOZbo8nPEq8kqN23BUgHftI8n0xU5bPn6XOtBuGMXuo2iIyOZi4zuPoo+HKKwF4H2FaZUpUjy3n6om9+3d07CDisXRkcH3YTqxvgZiskO4vrF1ChHXmuAHCOhub3g4p2xcirItifcI6Q4CwLooNEtZ1UikrnRXWpSkovecj66WeedWSsNUUne0Qa0mm1F5ArOutDlmIpspiMgHVQjL4hHURNQjryzvfDY4xDMODZa4zmLh2mZhI+QqDM9Vjy7l6bK9Mu7fUXhEhwtsdV8AkRmMeMCR7SCXLa5XYAPHsElI+LxNbZWXGAPHs4pvEWHjcAF92hhBvtUOQeHZja85Km7A2jPqpdQXVeYIpryQ33jijMAUuZF/OGn9e9EDPAtK7MTQbqYtPXEtHs+HYQxKrL46NpScCjY+ntxe5pfjSdpC0/WMMt9RedqXG+O9eVjp+Dm5ZPjdL7W4nM9PZjDfe7WQ2uSjD7Zv0mMlSjxWU00vGupMYizLPiax2UV/JqRAydYYKKoCIpz3TViPqqRCiKulhBpTt04w9xF96j05i1cc860iiq3QkZfORRHm9qI3cNuitmJhoT9pSElYRiC0eE05sZqXG6diOE5t87TRbmmpvTqbtIcnXUrOlqW+aolJ8zkqNSTtIqkxfJ/UNl+TYQ3rtstkt09dOf/PWcuwhrdaUlUTb7fSqjk7ZvoxdpNOFpJUkiU1qNAxjjmDiuiIZYU1gltqpDiKu5cPHIrdaSPnjZkvtlY8NEdaZ4wYI60xsgLB2CRHWmdgAYZ2hhLDuS4CwHiQ+YV0cXKVsn/NAiMXD6Ru0QIxbei9kYRq39F6FBWKakwHHdbLS7kqNPtzFY4LsIZnqIeXT8hnxHFCmbybC+vLu90vv3zCMsqjZQnIwcT1DFNjIGF+QfBtIP4LEs0uF2JAa19nYgKLPDiErMWaOW+FUBS2B7sZWWSFx5qeqkpd6kFlrH1WEd37WemaECOBMbAV7SBWLx/DsIRWecAV7SKVYy0obxmiiWCm+HExcB9IT1Z/nuVzd2ANpSOqNX5rN9I2gOW3TyKsWkrwoZcKxi4wlbB3jY+n9TkykYtWpAJK0hORVC0lqscjyMf1Ax1ctJMQOUpDRDqoW4ma4C1ZmTFk8irLWmTrWScuOu193oqKTmU62O4LXzVr7stiZvu6+irLaPlwN6wnNiOdMe+LvbqNvW9Tef5JjkdDWrkDy/5iwj0jSKgLQwbGDOBaQrkxdE9JJX0vSlmy1ENc+0qse0obOokTflmP5cC0gCTtJxkoyScbykbGPLIqrhWwutockX8PNye5Ue2Oym/qWqjHZSc3LkMl21i4ykbCHJGvtt1rOdtYu0vsWT91KI+02krCSaKeTto90OtF7a68tgWWtDWPEsA+/GUxcl6D3Vp4U1Xlk7CAB9hCZcFdiLP+vcReECZnEmBXP5VOvIXaQzHEDqoW4FC557qseEiKsM7EBwtohRFhnYueAsC5qy4hn35DzhLWn3UfGHhIQm7GHVLCWVLGHhGS4a7WHBC0uE7CKo0PWHjLz6iF5mLA2jMGh1D/xeD5Q5UvsgSMix4vIr0RkpYj8TU77IhH5btx+nYjsmWh7X/z4r0TkuJmOQYEfszfHykmc2HhZSlhLI+Am7wrtAPGcqRYyMZHfLwdXaId4q118pfaKqGLxqGQPGavwoq9gLalk8aiyEM2QcLPWYbFVKn44sRVWZpR2wOu5YFlzH5myfQHe6uZmt2xfSOk9/5LoPmRzBfHsCm93SXRfbFHW2jCMOc0o6LxBMLKZaxFpAmcBxwAPANeLyIWqenui2+nAWlXdV0ROAT4BnCwiTwNOAZ4O7AJcISL7q2qQ2bAnrM+Sg+MHEjcnaaTe6KXZzGynbBvSv1qI1w4C6b8nJtL2j/GxVCkznUhvp6qBLHKFtrtATCOVUewumm4vsoN0HL2frFqQaSuyfIz3b3MnLWbtIolz42a43Sy1K7yTFT+cLLQ0NZ30LchEJ7fdjHajyPKRjE2PMCeL7c9qp45bIPi7nkx1JhPtLiCTeG1kioW4mWgntpto1042ay3OdjrYiXUWj0n9jzsyvYgNaTEtXaeySDu9mIy0JfVhK/m5utGBrmMPSVo+pEVqgZjk+Wk41pLmZPq11XDsIckrojmp6Uojk449ZHM3ek3nkLGHbHbsIJs7aDIBkHzra7XT8z8mJ732kNT/b7KV+kZPW62+1UKi4MRrsqB6SB6WtTaMAaM6Y1vIKOi8QTHKmetDgJWqereqTgLnAyc6fU4Evhn/fQFwlIhI/Pj5qrpZVe8BVsb7K43GP1PCOoCsPSTgNLsZ7YAa126WWhcFVABxbsJJYV0Y6xHWRfiEdXBsQJbaJ6wLCRDWmdAAYe0yusLaExsgrAtjA+wh2Yy2Yw/xZKndrLTbNyRLLW6WOijDXaF6yGbHHtIKsIdsTt+bKtlDArLUbrWQKvWxDcOYPbSrfX8KGKrOGySjLK53Be5PbD8QP5bbR1XbwKPAjiVjCzlOTspvEKd8Xoi32qn4kfFa+3DsIBogvIuy1j6Kstb+WGccAd+VFGWtfRRlrb2xOVnrsmSEdpXY0pF+YV2ET1gXkRHaIcd1xXOFBWGqxIb4sF3clRiDxLOjO0PEc3PSFd4V7CGu5cODuOJ5svyg1VnVUav4si1rbRijg3b7//gZus4bFCNrCyFfW7h3kH59ysRGOxB5A/AGiP5byxNtN+oF+avKuHtyr58KJbbmCEuBh4c9iDmCnaty2Hkqj52rEkTJLTtXJbHzVJ5ROlf51RVmkcdYe+kVesFST5fFInJDYvtsVT07/ntWdN4wGGVx/QCwW2L7KcCDffo8ICJjwLbAIyVjAYj/yWcDiMgND6suz+tnTCMiN6idp1LYuSqHnafy2Lkqj52rcth5Ko+dqzSqenyF8FnRecNglG0h1wP7icheIjJBZFy/0OlzIfC6+O+TgKtUVePHT4lnme4F7Af8YpbGbRiGYRiGYfiZtzpvZDPXqtoWkbcClxLN0z9HVW8TkTOBG1T1QuAbwLdFZCXRJ5lT4tjbROR7wO1AG3jLqMwgNQzDMAzDWOjMZ50namvCTyEib0h4gYw+2Hkqj52rcth5Ko+dq/LYuSqHnafy2LkyymDi2jAMwzAMwzBqYpQ914ZhGIZhGIYxp1hw4rrKUpsLjRLn6lQRWS0iv4x/zhjGOIeNiJwjIqtE5NY+7SIiX4zP43+LzGBlonlCiXN1hIg8mrimPjjbYxwFRGQ3EblaRO4QkdtE5O05fRb8dVXyPNk1BYjIYhH5hYj8V3yuPpLTx+5/lD5Xdv8z+jKyExoHQZWlNmd/tMOl5LkC+K6qvnXWBzhanAt8CfhWn/YTiGYy7wc8H/hy/Hshci7+cwXwM1X9k9kZzsjSBt6lqjeJyNbAjSJyufP6s+uq3HkCu6YANgMvUtUNIjIO/D8RuVhVr030sftfRJlzBXb/M/qw0DLXVZbaXGiUOVcGoKrXEM1i7seJwLc04lpgOxHZeXZGN1qUOFcGoKoPqepN8d+PAXeQXX1swV9XJc+TAcTXyYZ4czz+cSdd2f2P0ufKMPqy0MR1laU2Fxpllxb98/gr6QtEZLecdmPEl2kdQf4g/jr2YhF5+rAHM2zir+YPAq5zmuy6SuA5T2DXFBB9IykivwRWAZerat9raoHf/8qcK7D7n9GHhSauqyy1udAocx5+Auypqs8CrmA642GksWuqPDcBe6jqs4F/BH405PEMFRHZCvgB8Fequt5tzglZkNdVwXmyaypGVTuq+hyi1ewOEZFnOF3smoopca7s/mf0ZaGJ65ClNpH0UpsLjcJzpaprVHVzvPk14LmzNLa5xkgv0zpKqOr63texqnoRMC4iS4c8rKEQez1/AJynqj/M6WLXFcXnya6pLKq6DlgBuEtX2/3Pod+5svuf4WOhiesqS20uNArPlePvfCmR39HIciHw2ri6w6HAo6r60LAHNYqIyJN7Hk8ROYToPWrNcEc1+8Tn4BvAHar62T7dFvx1VeY82TUVISLLRGS7+O8lwNHAnU43u/9R7lzZ/c/wsaCqhVRZanOhUfJcvU1EXko0Y/8R4NShDXiIiMi/AkcAS0XkAeBDRBNgUNWvABcBfwysBJ4AThvOSIdPiXN1EvBmEWkDG4FTFuLNHXgB8Brgltj3CfB+YHew6ypBmfNk11TEzsA340pQDeB7qvpTu//lUuZc2f3P6Iut0GgYhmEYhmEYNbHQbCGGYRiGYRiGMTBMXBuGYRiGYRhGTZi4NgzDMAzDMIyaMHFtGIZhGIZhGDVh4towDMMwDMMwasLEtWEYhmEYhmHUhIlrwzAMwzAMw6gJE9eGYRjGgkBEFg17DIZhzH9MXBuGYRjzGhF5joj8GnhCRM4XkfFhj8kwjPmLiWvDMAxjvvNV4Dqi5bwPBc4Y7nAMw5jPjA17AIZhGIYxYPYDTlTV34nInsBBwx2OYRjzGctcG4bRFxE5VUQ0FiQjjYh8OB5r72fxsMdklEdEjnb+f6WyyyJyhIi0RGQvT7cVwMtEZDvgeOB2Zx+vEpF1IrLjjJ+AYRhGjIlrwzDmG6+JfyaLOorImIj8nYjcIyKbROROEXmriMjgh1nPOERkT0eUJn++PqxxzWBstxL93z4WOMxPAN9R1Xs8fT4NnAWsBbrAPznt5wOrgQ8EHtswDCOD2UIMw5hXqOp3Arp/mch/+zXgF8CxwD8COwBn1j+6gY7jx8AFzmMrR2Bcpcamqr8DviMiRwDvL7NTETkKOAR4S0HXvwbuA3YF7lDV1AcvVe2IyFeBM0XkTFVdV+b4hmEYeZi4NgxjQSIizyYSjp9T1XfGD39dRL4PvF9EvqaqD82hcdwa+MFitsZV+9gSvB64S1Vv6NdBRP4A+FPgtUSZ6Wf16Xo+8EngVURZbsMwjBlhthDDmIOIyPaxR/RnzuM7iciv46/vd/DE7yEi/ygit4nIhvjnGhE5ruTxdxWRc0Xk9yKyWURuF5F3uHaBhGf7OBH5WxG5P7YX/Ecs3tz97i8iF4vI4yKyWkS+IiLPiPdxasnTU5aT499fcB7/ArCISJDNBrWNQ0SWiMiSURsX1D424nJ6LwEuK+j6SeCXwHeA24Bn5nVS1QeIvNh/XtcYDcNYmJi4Now5iKquJfKR/qGIHA0gIlsAPwG2BE5Q1Uc8u3gecAzwU+BdRF/xbwdcJCJH+o4dT/r6OfAK4Lw4/j7gs0SWgTw+Crw07nMmcCDwIxGZ+vZMRHYCrgFeCHwx7vdU4Fu+8VRgOfB7Vf2N8/gviHy5zx3QcQc1jrcDTxDVcr5LRIqsErM1rkGMjfj4WwDX9+sgIicCfwi8V1WVSFzvICK79gm5DjhUrA62YRgVMFuIYcxdPg+8DfiIiFwF/AvwdODwgsldABepasoDKyJfIMrwvRe42hP7XmB34CRV/UEcexbwA+AtIvJVVb0lJ+4wVW3H/e+M+x8LXJTY75OA41T1srjfPwFXFjyXmbIL8Fv3QVWdFJE1RP7c2aDqOLpE5+hHwG/i/Z0BfElE9lTVvx7SuAY5Nog+oAHcndcoIk3g/wBX9K4nInENkTUk89zifS0B9gTuqjA2wzAWMJa5Now5iqpuIBIPhwFXAC8GXq6qN5aIfaL3t4gsjrPRWxOVLHteQfhLgZU9YR3vT4FPxZsvyYn5Wk9Yx/TE+z6Jx14M3JkQQqhqB/hSwXhmyhJgc5+2TXH7bFBpHKp6n6oerapfUtWfqOpXiRZKuQZ4p4js44sf1LgGPDaAZfHvtX3a/xI4AHhP4rFb49/9fNdr4t9LK4zLMIwFjolrw5jbfBlYBxwJ/C9VvbhMkIiMi8iZInIvsBF4mKgU2ZuA7QvC9wTuzHm8Vzs4r95wyloQ21ogqjqR3G9etvB/CsYzUzYSeYfzWBy314KINEXkyc5PT5zWPo74Q8mnid7jj5rRoAd0fmoam5fYIvVh4BJgbVwScE+i8oxd+viusXuiYRg1YLYQw5jbvJPIKw2wPiDu88CbicT5/wMeATrAacArS8RrYFunT1+3XnJe7KBqTj9IjsgSkQlgx7i9LnYDXKvOacC5AxxH7wPNTLOwgzw/VccG0YdByP8w+A4iC8ouZM879M9c9/b1cIVxGYaxwDFxbRhzFBF5NdFEwTOJKjt8REQuiDODRbwS+JaqpiaWicjpJWLvZdrvmuTARPtMuBfYP+fx/Wa4vyJuBI4Rkd1V9b7E488jymAW2msC+B3RBNIkPf/voMaxb/x71QzjB3l+qo4N4I749z7AVNUcEVlKZAX5F+D7OXFvI5oIPK6qLadtH6KM/L0VxmUYxgLHvgIzjDlIXCHkHOBcVf0Q8PdElTVeW3IXHZzXv4g8lXLl1X4C7Csif5aIFeDdifaZcBFwgIgcm9hvk+IFQmbK9+Lfb3MefxuRfeBHiXGMi8gBIrLzTA6kqptU9Qrnp1cjutI48kouSrT0+/uBNsWl6vpR+fwMcGwQifsnyM4R+Dsi28p7VPVH7g/RNzXjRH5sl+cD1+WIbsMwjNJY5tow5hgi8iyiShsrgDfGD/8r8LfAh0TkPHcFuhz+DThdRJ4gEil7E9lE7gAOKoj9OPAXwL/GVULuJpqMeAJwVp9KIWX4BNECHv8mIl8ksh28DNgmbvdZUYJR1ZtF5ByiiXVbM70C4V8AH1HVpO1hV6Jz803g1BEbx2dEZHfgP4D7iSquvJYo4/+3TtZ5Nsc1sLHF42uJyE/jMQEgInsTzRs4V1XzqoHA9MqQzwKmrlUR2Y3o25cvz3RMhmEYYOLaMOYUsQC4mOhr65N6GTZV7YrImURfhb+R/vWme7yDqOLDy4DXEU1QfCORuPCKa1VdIyKHAR8jEkrbEAnsdwGfm9ETi/b7exE5nMgP3quLfAGRL/k/4/HWzZuIanSfRiQK742PXXT+RmkclwFviH92IDpvNwN/o6o/HOK4Bj02iJZlv1xElserNP4D0TcyH/fEJMX1eYnHTyaqjnJeJsIwDCMAiSpoGYZhjCax/eSHwAtU9eeefh8GPkRcok1VbVLaHCJeuGVb4AVElpPXq+rXC2KEKKN+q6qeVuHYTaIPmD9V1XfMdD+GYRhgnmvDMEYId3nsWPS8HXgUuKnkblYDq2NvrzF3OJzof/ejoo494vrq7wFeLSJ5JSDLcjKwE9EEYcMwjEpY5towjJFBRG4g8oD/F7AVcBLRhLV3q+pnCmL3JvKO97hKVbuDGqtRL/Hkx4MTD93u+LoNwzDmBCauDcMYGUTkg8DLiRaUGSP6qv5LqvqNYY7LMAzDMMpi4towDMMwDMMwasI814ZhGIZhGIZREyauDcMwDMMwDKMmTFwbhmEYhmEYRk2YuDYMwzAMwzCMmjBxbRiGYRiGYRg1YeLaMAzDMAzDMGrCxLVhGIZhGIZh1ISJa8MwDMMwDMOoif8PzN5U1dGUeJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 846x415.692 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_dict['SDVPN_solution'].E_gsf_surface_plot(length_unit=length_unit, energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_dict['SDVPN_solution'].E_gsf_vs_x_plot(length_unit='nm', energyperarea_unit='mJ/m^2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 Minimum energy per cycle\n",
    "\n",
    "minimization_energies in the results dict lists the total computed dislocation energy for the initial disregistry and after each minimization run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dislocation energy per minimization run (in eV/Å):\n",
      "0 5.344452611702983\n",
      "1 4.905268833362016\n",
      "2 4.9052634300504\n",
      "3 4.905263286909917\n",
      "4 4.9052631909361715\n",
      "5 4.905263105877672\n",
      "6 4.90526302164228\n",
      "7 4.905262935537678\n",
      "8 4.905262847023954\n",
      "9 4.905262756092103\n",
      "10 4.905262662820067\n"
     ]
    }
   ],
   "source": [
    "print('Total dislocation energy per minimization run (in %s):' % energyperlength_unit)\n",
    "for i, E_total in enumerate(results_dict['minimization_energies']):\n",
    "    print(i, uc.get_in_units(E_total, energyperlength_unit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Save/load results\n",
    "\n",
    "The full SDVPN results can be extracted and saved to either a JSON or XML file using the model() method of the SDVPN solution class.  This content can then be loaded into an SDVPN for future analysis or further runs.\n",
    "\n",
    "NOTE: The model saves all input parameters so the calculation can be restarted from any point. However, it only saves the current disregistry and not any previous disregistries before minimizations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model\n",
    "model = results_dict['SDVPN_solution'].model(include_gamma=True)\n",
    "\n",
    "# Save as json\n",
    "with open('sdvpn_run.json', 'w', encoding='UTF-8') as f:\n",
    "    model.json(fp=f, indent=4, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 846x415.692 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the saved model\n",
    "pnsolution = am.defect.SDVPN(model='sdvpn_run.json')\n",
    "\n",
    "# Show that the plot is the same\n",
    "pnsolution.E_gsf_surface_plot(length_unit=length_unit, energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
